The energy transition is one of the major challenges of the 21st century, impacting the way energy is generated, conserved and consumed. Energy generation becomes more and more decentralised, intermittency and fluctuations suddenly are becoming topics of interest within day-to-day life and energy system operators are facing many new obstacles never encountered before. In this context, the anticipation for hydrogen as a resource for energy conservation and -management is big. This research focuses on the optimisation of the hydrogen pathway for investment and operational models. The hydrogen pathway as aforementioned is divided over three technologies: hydrogen generation with means of water electrolysis, also known as 'green hydrogen', storage in compression vessels and reconversion of hydrogen into electricity in the form of a fuel cell technology (also known as Power-to-Gas). The research focuses on identifying technical parameters and operational policies of the water electrolysis systems that can be translated into optimisation constraints, assessing the level of detail required to create an accurate optimisation model. A generic model is developed that can be scaled for further research, making different case studies and sizing possible. The research compares the performance of the models in terms of accuracy to the computational burden. The comparison is done for the level of detail and complexity added to the model. After a literature review of technical parameters and operational policies regarding the technologies, two models were created in a mathematical framework. The two models proposed were Linear Programming (LP) and a Mixed-Integer Programming (MIP) Model. On the LP model 6 different sensitivity analysis has been performed, to be precise on Capital Expenditures (CAPEX), efficiency, lifetime, ramping rates, interest rates and finally different time horizons. The outcome of these analyses is that the technology mix can best be used in a combined manner, whereby each component of the mix contributes towards minimising the objective value: the Total Annualised Cost. Lastly the two models are compared with different types of configurations, each with a different set of constraints. The constraints to be modelled were: minimum uptime and downtime, start-up costs, degradation due to cycling and finally the part-load operation.