Discovery Early Career Researcher Award - Grant ID: DE220101277
Funder
Australian Research Council
Funding Amount
$427,600.00
Summary
Temporal-Spatial Data Analytics for Stochastic Power System Stability. The modern power system is evolving towards a renewable-energy dominated, digitalized "data-intensive" system, where enormous data are measured in multiple timescales, different locations, and in diverse structures. This project will develop a novel data-driven framework for power system stability analysis. This project will deliver new knowledge about instability phenomena and mechanism of power systems with high-level renew ....Temporal-Spatial Data Analytics for Stochastic Power System Stability. The modern power system is evolving towards a renewable-energy dominated, digitalized "data-intensive" system, where enormous data are measured in multiple timescales, different locations, and in diverse structures. This project will develop a novel data-driven framework for power system stability analysis. This project will deliver new knowledge about instability phenomena and mechanism of power systems with high-level renewable energies, faster-than-real-time system instability risk detection, and rule-based stability control. These research outcomes will form the basis of an innovative theoretical foundation to guide new technologies for power utilities for stability assessment and enhancement in the digitalized era.Read moreRead less
Exploring Emerging Collective Behaviours in Large-Scale Data-Driven Networked Systems. Understanding emerging collective behaviours in large-scale data-driven networked systems and developing methodology and approach for pattern identification and intervention are very important for high impact applications such as smart energy supply using smart meters. This project will propose a new theory for the developments, which will enhance Australia's leading position in this research and provide a cut ....Exploring Emerging Collective Behaviours in Large-Scale Data-Driven Networked Systems. Understanding emerging collective behaviours in large-scale data-driven networked systems and developing methodology and approach for pattern identification and intervention are very important for high impact applications such as smart energy supply using smart meters. This project will propose a new theory for the developments, which will enhance Australia's leading position in this research and provide a cutting-edge technology for industrial applications and training of the next generation of leading researchers.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