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Data analytics for Solar Microgrids

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  • Full or part time
    Prof Carol Boyle
  • Application Deadline
    No more applications being accepted

Project Description

Development of a digital model provides economic and performance advantages, including assessing performance under various conditions, developing predictive maintenance, testing ‘what-if’ scenarios and refining system efficiency. Comparing actual and theoretical operational performance between the model and the real system enables effective analytics to be developed which can be applied to other similar systems and support greater efficiencies and better cost/benefit returns.

A 7.25 MW solar microgrid including battery storage is being installed on the Waurn Ponds campus of Deakin University. To enable effective operation, maintenance and research to be undertaken for the microgrid, a digital model will be developed. The model will include a virtual representation of the microgrid and its connections as well as visualisations of operational and performance parameters. Analysis of the operational and performance parameters in conjunction with meteorological and user data will enable a digital model to be developed which ‘mirrors’ the operation of the microgrid and uses analytics to identify performance and operational anomalies over time. In addition, a ‘mirror’ digital model can be used to undertake ‘what-if’ scenarios and to test new equipment, software and adjust operational parameters to optimise performance. The proposed research will use existing data and software to develop and test analytics for a digital model of the Waurn Ponds energy microgrid.

Data analytics will be used to design optimum strategies for microgrid operation by controlling load demand and charging/discharging processes of the battery storages. The impact on the asset (e.g., solar panels, battery, transmission lines) lifecycle will be evaluated using analytics and embedded in the optimization strategies. Moreover, the analytics will unleash the potentials of new microgrid applications including energy sharing and ancillary market services. As a result, the findings and applications can be incorporated and tested for other residential/commercial microgrids of similar nature.

This research will involve development and testing of data analytics to develop a ‘mirror’ digital model of the Waurn Ponds microgrid. The analytics will enable ‘what-if’ scenarios as well as evaluate performance and operational parameters, guiding the development of optimum strategies for microgrid operation.

Funding Notes

3 year fixed-term appointment, with possible extension for 6 months. Stipend can range from $27,596 -$35,000 p.a. full-time rate (pro-rata) and tax-free. Australian or international applicants will be considered.

References

Eligibility Criteria:

1. Applicants must meet Deakin's PhD entry requirements. Please refer to the entry pathways to higher degrees by research for further information: https://www.deakin.edu.au/research/become-a-research-student/research-degree-entry-pathways
2. Applicants must be enrolling full time and hold an Honours degree (first class Honours) or an equivalent standard master's degree with a substantial research component in Engineering, IT, Data Analytics or a related field;
3. Applicants who are nearing completion or who are in the workforce are also encouraged to apply. Australian or international applicants will be considered.
4. Applications must have a systems thinking capability with a strong mathematics and problem solving focus;
5. Work experience in infrastructure (e.g. industry, consulting or government), such as solar microgrid control systems, data analytics, energy network analysis will be balanced with the academic record.
6. The following criteria will be taken into account during the assessment:
• Candidate’s academic performance in the bachelor’s degree (or Master’s degree);
• Knowledge in the relevant research field (applied and publications);
• Familiarity with Matlab, Python, R or similar software;
• Demonstrated ability to work as part of a multi-disciplinary research team;
• Organised and self disciplined;
• Understanding of sustainability, social and cultural contexts;
• English language proficiency (this position requires strong written and verbal communication skills); and
• Online interviews and references.
7. Applicants must provide a copy of their CV and Academic Transcript upon expression of interest.



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