Our Research

The Optimisation in Energy Networks research is concerned with using mathematical optimization to provide guaranteed optimal, or near-optimal, solutions for important classes of large-scale discrete nonlinear optimization problems arising in engineering applications. In particular, mathematical optimization can help to improve the overall performance of electric power systems, which are of critical importance to sustainability. 

An example is the development of smart grids. These combine a traditional electrical power production, transmission, and distribution system with a two-way flow of information and energy between suppliers and consumers. This combination is expected to deliver energy savings and cost reductions, which are both key in keeping energy sustainable.  

Alongside decarbonisation, another key challenge for society is maintaining resilience of critical infrastructure in a changing climate. This brings many modelling challenges deriving from understanding the future path of a physical system as complex as the climate, and relating our knowledge of the future climate to infrastructure systems which are also very complex.

One part of the School’s contribution to this agenda is providing technical leadership within the National Digital Twin programme’s Climate Resilience Demonstrator, which considered the necessary analysis for local-area resilience across all of climate data, hydrology for flood analysis, asset vulnerability, system consequences – with particular emphasis in the context of the DT programme on the data and model interoperability required to link these aspects together.

We are also working on national level energy security of supply in a changing climate, a topic which is particularly prominent heading in to winter 2021-2 due to concerns over fuel supplies and prices. This collaboration with the School of Geosciences and colleagues at the Alan Turing Institute studies how the relationship between temperature and wind resource may change in the future, and how one can use this knowledge in assessment of security of supplies given inevitably limited data on relevant extremes of simultaneous low temperature and low wind resource.

The structure of our buildings acts as a sort of thermal storage, and it’s charging cycles can be optimised to achieve any goal. There are many factors which affect heating demand; therefore, we propose an optimisation framework that represents the brain of smart buildings. We call this TEMS (Thermal Energy Management System), and it aims to be an affordable solution to reduce energy consumption and decarbonise cities by providing flexibility to the grid.  

We also study building refurbishment; this is important because of the need to reduce our energy consumption. We model a framework that outputs the optimal actions, such as optimal values of insulation, to renovate a building. This is intended to be a tool used by both architects and engineers. I was able to test my knowledge in real projects whilst working as an intern with the University of Oxford on project LEO (Local Energy Oxfordshire). The aim of project LEO is to find evidence of the technological, market and social conditions needed for a more sustainable and affordable energy system. Alongside my PhD, I also work as a sustainable engineer intern in the IES company (Integrated Environment Solution). IES is a software and consultancy company that work to make our cities more sustainable.