Renate Egan is Professor Deputy Head of School (Engagement) in the School of Photovoltaics and Renewable Energy Engineering. She leads the UNSW activity in the Australian Centre for Advanced Photovoltaics, the national centre for photovoltaics research led by UNSW, in partnership with ANU, UQ, CSIRO University of Melbourne and Monash, to work together on the next generation of photovoltaics.
Renate joined UNSW in 2014 after a 20-year career in industry where she led manufacturing and technology development in Australia, Germany and China and is a leading authority on manufacturing costing and technology transfer. Renate is also Co-Founder of Solar Analytics, Australia's largest independent energy monitoring provider. In earlier roles, as Director and CTO of CSG Solar AG and Managing Director of Suntech R&D Australia Pty Ltd, Renate held executive leadership roles in technology development in the solar industry at a time of dramatic progress in manufacturing, development and deployment.
With expertise across industry, manufacturing and small business, government, university and not-for-profits, Renate now participates on a number of national and international panels, boards and review committees across the energy sector. Renate represents Australia on the Executive Committee of the IEA PV Power Systems program.
Major Research Themes:
Techno-economic analysis - Bringing together a lifetime of experience in taking technology to market, this project focuses on developing models and metrics for assessing new technology developments for their commercial viability and impact on energy markets. The research is cross-disciplinary, taking into account engineering developments, financial modelling, energy markets and environmental and social impacts.
Energy Data for Smart Decision Making - As the energy industry shifts from centralised to decentralised generation, the use of good data becomes critical for decision-making. With a deep understanding of the cost trajectories in solar deployment, and with increasing visibility of energy data in networks, this research seeks to use machine learning and big-data analysis to understand energy generation and demand patterns, including elements of generation, storage and forecasting.