The Dutch heating sector, responsible for 41% of national energy use, remains heavily reliant on fossil fuels. In light of climate goals, this thesis explores how integrating district heating systems with the electricity grid, particularly using geothermal energy and high-temperature aquifer thermal energy storage (HT-ATES), can reduce operational costs and improve flexibility and sustainability. Using a techno-economic optimization model, a real-world case study of Delft was analyzed across four scenarios: fixed and dynamic electricity pricing, grid constraints, and grid reinforcement. The results show that dynamic pricing enhances system responsiveness and storage value, while thermal storage proves value in shifting demand, reducing gas reliance, and improving grid stability. Grid limitations significantly impact electric heating, but storage offers a viable short-term mitigation. A trade-off is evident between capital costs, system resilience, and emissions: larger systems reduce CO₂ but require higher investment.

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

The transition to a carbon-neutral power system has become a global imperative, ever more driven due to recent events such as the energy crisis resulting from the Ukraine invasion. The Netherlands, devoted to the large-scale development of offshore wind and solar PV, see great potential in the use of hydrogen as a gas replacing energy carrier. A knowledge gap has been identified on what this role of electrolysis will be in the future energy system and more specifically, in what quantities electricity can and should be converted to hydrogen. This research aims to address these uncertainties and provide insights into the prudent capacity for green electrolytic hydrogen production in the Netherlands by 2030 and 2040.

To this end, a model of the combined electricity and hydrogen system of the Netherlands is constructed in Linny-R, an executable graphical representation language for Mixed Integer Linear Programming (MILP). A vast array of output data from forecasting scenarios is gathered and consolidated to establish the spectrum of input variables. By systematically assessing the uncertainty, based on these ranges of input variables, and the impact, based on a sensitivity analysis, an experiment design is composed to encompass the most complete reflection of all uncertainties within computation limits. Numerous experiments are then conducted to generate outcomes, determine prudent capacity, identify influential uncertainties, and evaluate the impact of large-scale hydrogen storage on electrolysis potential.

The research results demonstrate that the potential for electrolysis increases from 8-12 GW in 2030 to 8-44 GW in 2040, indicating the cruciality of flexibility for the Dutch energy system in the coming years. The most prudent capacities for 2030 and 2040 are identified as 12 GW and 38 GW, respectively, with 9 GW being identified as the no-regret capacity to be installed by 2030. Therefore, the research recommends stimulating additional investments beyond the initial targets of 3-4 GW for 2030 to foster the Dutch hydrogen economy. Moreover, there is substantial risk of overinvestment associated with the prudent capacities, especially as a result of the growing uncertainty in 2040. Deploying additional flexible system capacity to prevent electrolysis from acting as a loss of load mitigation measure can lower the prudent capacity and reduce the risk of overinvestment. Furthermore, policy measures should focus on stimulating influential uncertainties such as hydrogen demand, peak residual load and hydrogen storage. Every additional 100GWh of underground hydrogen storage capacity enables 500MW of electrolysis potential. Moreover, the last few gigawatts of the prudent capacities will primarily serve as peak production plants with limited operational hours and margins starting at 11.00 EURO/kg required to recoup the investments.

Further research opportunities include incorporating geographical and grid aspects into the model, exploring profitable conditions for investments in the last few GW of electrolysis capacity, investigating the role of large-scale hydrogen storage, and studying the potential of electrolysis beyond national borders in the North Sea interconnected electricity generation hub.