Coordinated cross-sectoral decision making under uncertainty and risk aversion for effective decarbonisation

Project ID: 2531ad1582

(You will need this ID for your application)

UCL Lead department: Statistical Science

Project Summary:

Why this research is important?

Policy development for the transition towards a decarbonised society has largely neglected deep cross-sectoral effects of decarbonisation policies, despite the widespread understanding that such a transition requires whole-system thinking. This project will develop novel OR and statistical methodologies for optimisation under uncertainty to investigate how coordinated decision-making policies between actors in power, heat, transport and adjacent sectors can be used for effective decarbonisation of energy systems. Doing so is challenging for a number of factors, including the actors’ conflicting objectives and competitive interactions; the various sources of uncertainty surrounding market factors (e.g. prices and demand) and those related to technology and its adoption; decision maker’s risk aversion; changes to market designs in renewable energy-based systems such as zonal energy pricing, as well as constraint and capacity payments; and, lastly, the role of prosumers, e.g., in energy communities.

Who you will be working with?

The supervisory team consists of Dr Sebastian Maier (UCL Department of Statistical Science) and Professor Afzal Siddiqui (UCL Department of Statistical Science, and Department of Computer and Systems Sciences at Stockholm University).

What you will be doing?

To achieve these ambitious goals, this PhD project will leverage various OR and statistical methodologies potentially including (but not necessarily limited to) stochastic optimisation, data-driven approaches, bi-level programming and game/decision theory as well as stochastic and equilibrium modelling. Insights from applications of the novel framework to case studies will support devising policy mechanisms to mitigate distortions to welfare, prices, and emissions

Who we are looking for?

Prospective applicants should have strong quantitative skills and evidence of research experience (e.g., MSc dissertation project), but there is flexibility to take a candidate from a range of quantitative backgrounds (incl. mathematics, OR, statistics, economics/finance, and computer science), adjusting the precise direction of PhD research accordingly.