Quantitative Modelling & Optimisation under Uncertainty for Energy and AI Infrastructure
PhD in Systems Modelling and Optimisation, University College London
Optimisation, Monte Carlo simulation, scenario analysis, sensitivity analysis, stochastic decision models.
Infrastructure sizing, energy demand, storage, resilience, techno-economics, resource allocation.
PEM electrolysis, degradation, lifecycle economics, hybrid energy storage.
Mixed-integer sizing optimisation of a battery–hydrogen hybrid storage system for data-centre reliability, with Monte Carlo uncertainty quantification over multi-decade planning horizons.
Assessment of the dominant factors governing performance, durability and cost, linking electrochemical degradation to replacement cycles and lifecycle economics.
Technical and economic performance assessment of a blue hydrogen production configuration through process modelling and simulation.
Degradation-aware assessment of dominant factors in performance, durability, and cost of proton exchange membrane water electrolysers
International Journal of Hydrogen Energy 264, 156855 (2026)
System modelling and sizing optimization of PEM-integrated hybrid energy storage for data centre resilience
IET Conference Proceedings CP963, 2025 (44), 149–154
Technical and economic performance assessment of blue hydrogen production using new configuration through modelling and simulation
International Journal of Greenhouse Gas Control 134, 104112 (2024)
Open to conversations on quantitative research, infrastructure modelling and energy systems.