The swift reduction of human’s carbon footprint is essential to prevent irreversible damage to the climate and to meet climate policy targets. Designing flexible and reliable future energy systems is a big contributor to meeting these goals. While energy system models have improved in the last few decades, they remain vulnerable against parametric and structural uncertainty due to the varying characteristics of parameters and the hardship of modelling all constraints and drivers accurately. This thesis proposes a method that addresses both uncertainty types in energy system modelling by applying SPORES cost optimisation and Monte Carlo scenario modelling simultaneously.

The main case study uses 27 input scenarios with varying outcomes for grid electricity price, solar yield and energy consumption to provide insight in a 100 household neighbourhood energy system with heating, cooling, electricity and hydrogen as energy carriers. With 1377 (near-)optimal solutions, a novel approach in analysis and post processing is used to provide 52 useful configuration options that each have their strengths and weaknesses to different political, economical, social and technical drivers. These configurations are tested for cost, security of supply, CO2 emissions and grid dependency. Those results are visualised through ridge plots and statistical tables to provide a clear overview between each configuration’s trade-offs. An example is included to show how those results can be used for improving energy system design in practice.

This thesis shows that two methods can successfully be combined into one universal one, while providing valuable design insights for energy systems under uncertainty. Furthermore, this method can be applied to a wide variety of energy systems, as long as its possible components, their technical aspects and their allowed interactions are known beforehand. As many future energy system aspects are uncertain, it should be seen as a vital tool to help speed up the decarbonization.