Power quality monitoring of grids with high penetration of power converters. The project aims to monitor and analyse power quality of grids within the frequency ranges of 0-2 kHz (existing regulations) and 2-150 kHz (new regulations). Power quality of grids deteriorate due to the high penetrations of inverter-based renewable energy systems. To estimate power quality of grids, the project expects to develop a multi-domain simulation model based on power grid configurations and operating condition ....Power quality monitoring of grids with high penetration of power converters. The project aims to monitor and analyse power quality of grids within the frequency ranges of 0-2 kHz (existing regulations) and 2-150 kHz (new regulations). Power quality of grids deteriorate due to the high penetrations of inverter-based renewable energy systems. To estimate power quality of grids, the project expects to develop a multi-domain simulation model based on power grid configurations and operating condition. Developed methodologies will assist network service providers to better analyse harmonics and resonances within low and high voltage power systems. Expected outcomes of this project are to assist partners to monitor and solve the existing communication issues of audio frequency load control and to address power quality issues arising from the increasing connection of renewable energy systems.Read moreRead less
Smart house energy management system. This multidisciplinary project will empower Australia's power industry with tools and knowledge that will enable the transformation to be more intelligent and flexible. It will help reduce greenhouse gas emissions and increase energy efficiency by smarter use of the resources at household level.
Transforming Microgrid to Virtual Power Plant –ICT Frameworks,Tools,Control. The project aims to enhance large scale renewable penetrations to national power grid by advancing control, optimization, and ancillary services of Virtual Power Plants (VPPs), considering different disruptive events including recent South Australian blackout. This project expects to create new control, frame communication architecture, develop plug and play type IoT enabled grid interfacing inverter, and optimize resou ....Transforming Microgrid to Virtual Power Plant –ICT Frameworks,Tools,Control. The project aims to enhance large scale renewable penetrations to national power grid by advancing control, optimization, and ancillary services of Virtual Power Plants (VPPs), considering different disruptive events including recent South Australian blackout. This project expects to create new control, frame communication architecture, develop plug and play type IoT enabled grid interfacing inverter, and optimize resource management for distributed VPPs. The anticipated benefits from this institutional level collaborations are that VPPs help in enhancing national power grid operations during normal and disruptive conditions when more renewables are connected and also secure benefits of consumers, prosumers, and grid operators.Read moreRead less
Distributed control for wide-area demand response. This project underpins the paradigm shift from load following to load shaping in power system operation by unlocking the untapped potential of the demand side. The approach taken is to use modern ideas in distributed control. This will facilitate large-scale integration of renewable energy sources and thus render the energy supply more sustainable.
Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in ....Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in Australia's electricity distribution network planning and operation by considering future challenges such as integrating battery storage and electric vehicles into the grid, and thus providing reliable energy. The project expects to train next generation expert workforce for Australia's future power grid.Read moreRead less