Interests: Statistics, renewable energy, machine learning, optimisation.
My PhD focuses on using statistics to make the best decisions in the energy transition, particularly in the electricity sector. Such decisions (e.g. whether to build a wind farm, battery or new transmission line) are typically difficult due to the complexity of electricity grids and considerable uncertainty regarding future government policy, grid developments, electricity demand/price patterns and weather events.
For this reason, electricity strategy is typically informed by large amounts of data. For example, to determine the best location of a new wind farm, historic wind speeds and electricity prices may be employed, along with a model of the grid. This leads to a natural statistics problem: how do we use the available data to make the best decisions in the energy transition?
See Google Scholar page for up-to-date publication list.
AP Hilbers, DJ Brayshaw, A Gandy (2023). Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage. Applied Energy, 334, 120624. [open access paper] [code]
AP Hilbers, DJ Brayshaw, A Gandy (2021). Efficient quantification of the impact of demand and weather uncertainty in power system models. IEEE Transactions on Power Systems, 36-3, 1771-1779. [paper] [open access version] [code]
AP Hilbers, DJ Brayshaw, A Gandy (2020). Importance subsampling for power system planning under multi-year demand and weather uncertainty1. In proceedings of the 16th IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS 2020). [paper] [open access version] [code]
1Runner-up for Roy Billinton Award for best student paper
2See 2020 importance subsampling paper for updated version