Maximising renewable energy for water corporations through machine learning, peer-to-peer trading and demand management

Supervisors

Dr Rebecca Yang and Dr Kazi Hasan (RMIT)

Ruben Muller (Sydney Water)

The project

Water corporations have extensive electricity demands, which lead to high energy bills and significant carbon emissions. Sydney Water is aiming to achieve net zero carbon emissions by 2030. Meeting this goal requires identifying suitable and practical demand management options under different RE adoption scenarios, considering their economic benefits and avoiding detrimental effects on the local electricity grid.

This PhD project will explore the potential to achieve net zero emissions using diverse demand management and renewable energy adoption scenarios. This will include developing software to facilitate decision-making for finding optimal strategies from both technical and economic perspectives for water corporations.

Through this study, water corporations are expected to reduce their carbon emissions by approximately 30% and energy bills by 27%. This will help water corporations to maximise economic benefits while working towards zero carbon emissions from their business operations, contributing effectively to governments’ energy goals.

The successful PhD applicant will work closely with the research partner on the PhD project and will undergo a 2 day commercialisation training

Eligibility

To be eligible for a RACE for 2030 scholarship:

1. Applicants should have a first-class Honours or Masters degree or equivalent in a related discipline, OR a combination of an upper second-class Honours degree or equivalent in a related discipline together with a minimum of five years equivalent full-time professional work experience in a relevant field.
2. Applicants must be eligible for enrolment in their chosen course at RMIT. It is recommended that students obtain relevant postgraduate information from the relevant university before pursuing a scholarship inquiry.
3. Applicants must be studying full-time.