Research Interests
I am interested in how faithfully the models we use to plan energy systems capture the physical reality they stand for, and in what we lose when we simplify it. Simplified assumptions are often necessary, but they can quietly distort the decisions a model reaches. Much of my work is about understanding when this matters, and building models that are more faithful without becoming intractable.
In my PhD, I study how machine learning and mathematical optimisation can be combined to represent the detailed, often non-linear behaviour of energy technologies inside these large models. As of late, I have been working on surrogate models of electrolysers for green hydrogen production, monotonic networks for estimating the state-of-health of lithium-ion batteries, and using the power system model highRES to test how sensitive the design of an interconnected European power system is to reliability and adequacy assumptions.
Background
Integrated Master's in Physics Engineering, with a specialisation in asteroseismology, at Instituto Superior Técnico, University of Lisbon