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Data-driven analysis of energy systems


Project Description

The School of Mathematical Sciences of Queen Mary University of London invite applications for a PhD project commencing either in September 2020 for students seeking funding, or in January 2020 or April 2020 for self-funded students. The deadline for funded applications is the 31st of January 2020. The deadline for China Scholarship Council Scheme applications is 12th January 2020.

This project will be supervised by Professor Christian Beck and Dr Benjamin Schaefer.

The electrical supply and the power grid are integral parts of the our modern society. Without access to electricity, farmers would not be able to feed their animals, car factories would come to a halt, mobile phone systems would fail and many of us would not even be able to make a cup of tea. While the current power system is very reliable and offers a high quality of service, it remains unclear how this will develop in the future. The limited supply of fossil fuels as well as necessary reduction of CO2 emissions to mitigate climate change, will eventually lead to a power grid mainly supplied by renewable generators, such as wind and solar plants. These plants output smaller total power so that a large number is necessary which have to be geographically distributed for optimal weather conditions. The current power grid system slowly emerged within several decades of optimization processes. However, now we are discussing how to revolutionize the whole energy system within years. Therefore, a fundamental understanding of the current power system is necessary to develop potential pathways to a future 100% sustainable system.

We are investigating various questions within this complex topic of energy research, using stochastic analysis and data-driven approaches to work towards a quantitative understanding of different aspects of the energy system. This includes a detailed analysis of fluctuations in the power grid itself, analysis of renewable generators, demand trends and fluctuations, interaction with the energy market and more. To achieve a better understanding, we perform statistical analysis, e.g. inspecting histograms but also run more detailed stochastic analysis, e.g. to test the Markov property with Chapman-Kolmogorov tests. In addition, we formulate the underlying dynamics in mathematical models to predict for example renewable generation or future demand. A recent development is also the usage of machine learning techniques as forecasting tools. For these tasks, we are continuously looking for interested students

The application procedure is described on the School website. For further inquiries please contact Professor Christian Beck at and Dr Benjamin Schaefer at . This project is eligible for full funding, including support for 3.5 years’ study, additional funds for conference and research visits and funding for relevant IT needs. Applicants interested in the full funding will have to participate in a highly competitive selection process.

Funding Notes

This project is eligible for full funding, including support for 3.5 years’ study, additional funds for conference and research visits and funding for relevant IT needs.

This project can be undertaken as a self-funded project. Self-funded applications are accepted year-round for a January, April or September start.

We welcome applicants through the China Scholarship Council Scheme (deadline for applications 12th January 2020).

The School of Mathematical Sciences is committed to the equality of opportunities and to advancing women’s careers. As holders of a Bronze Athena SWAN award we offer family friendly benefits and support part-time study.

References

B. Schäfer, C. Beck, et al, Non-Gaussian Power Grid Frequency Fluctuations Characterized by Lévy-stable Laws and Superstatistics,
Nature Energy 3, 119-126, 2018

L. R. Gorjão, M. Anvari, H. Kantz, C. Beck, D. Witthaut, M. Timme, B. Schäfer, Data-driven model of the power-grid frequency dynamics, ArXiv preprint: 1909.08346

Related Subjects

How good is research at Queen Mary University of London in Mathematical Sciences?

FTE Category A staff submitted: 34.80

Research output data provided by the Research Excellence Framework (REF)

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