Summary

This thesis is part of the broader “HyChain” research project, which investigates the deployment of hydrogen in the Netherlands. Hydrogen is a versatile energy carrier, and it may offer a viable path towards achieving a low-carbon economy. However, there are still many open questions regarding the deployment of hydrogen in the Netherlands.
Mathematical optimization provides a systematic framework for answering such questions by modeling variables, objectives, and constraints in a transparent and quantitative manner. However, its application to real-world problems is complicated by uncertainty. This thesis focuses on two major types of uncertainty: parameter uncertainty, which arises when input data (such as costs or demand) cannot be predicted with certainty, and model uncertainty, which occurs when the mathematical formulation does not fully represent the real-world system.
Our applications to hydrogen deployment in the Netherlands demonstrate the practical value of our methods in bridging the gap between mathematical optimization models and the uncertainties and complexities of real-world problems.
While the methodological contributions of this thesis are motivated by questions related to hydrogen deployment in the Netherlands, the methods developed herein are broadly applicable to any problem context involving mathematical optimization under uncertainty.