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
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