With many countries rapidly electrifying their energy consumption, distribution network infrastructure will need costly upgrades. However, there is significant uncertainty surrounding the changes in energy consumption over the next decade. The infrastructure and policies needed depend on the degree to which low-carbon technologies are adopted (and where they are installed). Understanding where network infrastructure is most needed will help accelerate the decarbonisation of a nation’s energy consumption.
My research involves modelling British low-voltage distribution networks with probabilistic programming techniques. The reasons for using probabilistic programming are due to its ability to:
• determine the likely impact of different scenarios via Bayesian inference,
• model systems hierarchically, helping capture the heterogeneity of distribution networks,
• model the network with limited information and significant uncertainty, and
• incorporate prior knowledge and forecasted trends for analysing future scenarios.
I am particularly interested in how decision-makers can utilise data without depending exclusively on it, so that novel situations and unrepresented phenomena can still be appropriately accounted for. Since we don’t have data on future power usage patterns, relying solely on a data-driven approach to investments and demand-side management would be irresponsible.
Additionally, it is important that this research, as it looks to inform the design of consumer electricity pricing schemes, be transparent and auditable. By eschewing black-box techniques, and instead building models with interpretable variables, unjust correlations between different household attributes and network usage costs can be easily determined.
Probabilistic programming with Julia.
Bayesian data analysis.
Electric power distribution.
MSci in Natural Sciences specialising in Physics, University of York
Medical Microwave Imaging Research, University of York & Sylatech
Prof Furong Li
Dr Julian Padget
Dr Xi Chen