Climate change demands a transition to a carbon-neutral energy system. In the Netherlands, this shift involves electrifying sectors, predominantly by increasing energy sources like solar PV and wind power. This comes with flexibility challenges during times with insufficient variable Renewable Energy Sources and extended periods with wind droughts, or a dunkleflaute which is a period of more or less two weeks with insufficient wind and a cold front. Electricity generation from molecule based fuels can play this flexible role to balance the system in the future. Hydrogen is a versatile energy carrier that can play this role in a low carbon manner. Hydrogen can be stored in large volumes underground in salt caverns and in depleted gas fields. To ensure security of supply in different scenarios it is necessary to understand what volume of hydrogen should be stored in storage facilities to withstand a dunkleflaute. What is also necessary is to understand with what speed this hydrogen should be injected and produced from storage facilities, in other words: in how many days the storage can empty and fill. The production of green hydrogen with which the storage is filled is produced through the process of electrolysis. There are different types of technologies to produce green hydrogen. This research also focuses on the trends in developments regarding electrolysers and their impact on the system. As well as on the policy goals set by European Union and the Dutch government. The method in which the research was set up is by determining the system characteristics and uncertainties and going through the motion of the modelling cycle multiple times. This research is conducted by simulating the Dutch energy system in Linny-R, a mixed integer linear programming software tool. The model is made for the years 2035 and 2050. A sensitivity analysis is done to determine the most influential keys in the system. Based on literature and this sensitivity analysis, a scenario analysis was determined. Many scenario’s were then tested in the model to determine their impact. The system is measured with the following KPI’s: Carbon emissions, Loss of Load Expectation and Expected Energy Not Supplied, System costs and Production cycles. The conceptualisation in this thesis details the intricacies of the Dutch energy system and its simulation within the Linny-R model, offering a foundational understanding essential for later analyses. This chapter dissects the components and operational settings of the model, emphasizing their significance in shaping outcomes. The Dutch energy system’s transition from fossil fuels to renewable energy sources is mapped within Linny-R, highlighting the interplay of various energy forms and the constraints inherent to the system. The setup includes explanations of Linny-R’s key entities and discusses the model’s execution over a yearly timeline with hourly steps to capture dynamic system behaviours. Special attention is given to the settings impacting computational efficiency and the incorporation of historical weather data to account for variability in renewable energy production. After discussing the system a framework is presented, detailing the external factors, policy levers, system relationships, and performance metrics that guide the analysis. The analysis focuses on verifying the model, conducting a sensitivity analysis, and beginning scenario discovery to test the model’s performance. Verification ensures the model accurately represents real-world systems by examining storage facilities’ behaviors and hydrogen-to-power conversion during low variable renewable energy supply (vRES) periods. Sensitivity analysis tests how changes in input parameters impact outputs, identifying critical inputs that influence the model’s performance and ensuring robustness. Key findings show that hydrogen and electricity demand parameters significantly affect system costs and efficiency. Scenario discovery assesses the model’s resilience under various conditions, including extreme weather years, to identify potential bottlenecks. This chapter establishes the model’s reliability and guides improvements in its predictive capabilities. The results show the findings from the scenario analysis aimed at evaluating the potential of underground hydrogen storage in the future energy system. These experiments investigate various performance parameters to inform decision-making for effective integration of underground hydrogen storage. Key findings include the impact of electrolyser capacity on hydrogen production and system efficiency, ii iii revealing that increased electrolyser capacity significantly reduces carbon emissions, though it must scale parallel to storage capacity to be effective. The analysis of different storage types highlights the importance of sufficient electrolysis capacity to fully utilize storage facilities. Concluding their dependence on one another. Additionally, scenarios exploring battery capacity, gas power plant use, and demand variations provide insights into optimizing the energy system while achieving policy goals. The results underscore the complex interplay between various components of the hydrogen storage system, emphasizing the need for balanced and integrated approaches to meet future energy demands and sustainability targets. The discussion interprets the research findings, emphasizing the anticipated demand for underground hydrogen storage and the required production capacity for 2035 and 2050. The study reveals that while there will be significant storage needs in 2035 due to hydrogen shortage, the demand will decrease by 2050 as renewable energy sources and electrolysis capacity increases. Economically and functionally, a combination of salt caverns and gas fields is recommended, with optimal configurations identified for injection and production capacities. The research also highlights the critical role of electrolysers and their efficiency. Limitations of the study are discussed and recommendations for future research directions suggest exploring blue hydrogen, integrating hydrogen flow dynamics, and considering regional energy needs. The study’s findings have practical implications for policy decisions for Dutch hydrogen storage