Exactly predicting what the future energy system in the Netherlands will look like in 25 years is impossible, but there are ways to explore possible scenarios. A widely used tool to help give an idea of how such a system will look is energy system optimisation modelling (ESOM). TenneT, the Dutch TSO, also uses optimisation modelling to explore possible cost-effective plans for this future network. A challenge they face is not the ability to run intensive models but the need for a model that can rapidly generate a wide range of alternatives of interest. While highly detailed results with high resolution are necessary, in many cases a model is more useful when it can produce results quickly and efficiently without the need for long computation times.
This research aims to develop a flexible and exploratory energy system model for the Netherlands in 2050. This enables analysing a large set of alternatives without the high resolution of a detailed model that causes long running times. Additionally, with the modelling approach Modelling to Generate Alternatives (MGA), specifically the SPORES method, we can uncover a range of near-optimal solutions to provide insight into the technical composition of the model. With a large number of alternatives, different technology choices, technology trade-offs and spatial capacities can be examined. To create this model, the energy system modelling framework Calliope was used. The literature provides research gaps and opportunities in the current methods of using MGA. The focus for this research is aimed at developing a method to be able to create large sets of solutions without the need for long modelling times. Desk research and secondary data analysis gave insights on the current state of the Dutch energy system, and conducted interviews ensured the model was a good fit for the company and their vision of the model. The interviews resulted in a set of six questions that could be answered using MGA.
The model consists of a twelve-node Dutch energy system with both electricity and hydrogen carriers included. It also has connections to the neighbouring countries to represent the import and export of energy. The model results project a high reliance on renewable energy technologies like offshore wind and solar PV. The model also uses several types of energy storage, namely salt caverns for hydrogen storage and Li-ion batteries for electricity storage. Especially hydrogen storage is used as the main flexibility technology of the system. In comparison with the II3050 modelled energy system, the model outputs show lower use of offshore wind and more use of hydrogen. The results also show a promising possibility for Small Modular Reactors (SMR). The transmission network will need to be expanded to handle large amounts of energy from Groningen to Noord-Holland and from Zuid-Holland to Limburg. The SPORES results demonstrate that not all technologies are mandatory for the energy system. Some technologies are interchangeable within a small cost margin. BECCS-rebuilt and SMRs frequently replace hydrogen technologies or renewables in alternative configurations. This change in technique compositions also has an impact on the transmission system. Although certain reinforcements appear consistently. Directed SPORES runs show that assumptions about affordability, nuclear expansion, decentralisation, or BECCS-rebuilt availability significantly affect system architecture. Limitations of the model include fixed electricity import/export prices, costless and lossless hydrogen transport, and GDP-based spatial demand allocation. Despite these simplifications, the model allows rapid exploration of structural differences across scenarios.
This thesis advances the field of energy system optimisation modelling by integrating qualitative knowledge from expert interviews with quantitative scenario generation. The study shows how system planning questions can be addressed more quickly by using MGA to generate rapid results. It demonstrates how the MGA method SPORES can be used to generate insight into system design flexibility, infrastructure needs, and guided searching. Methodologically, the research highlights the accessibility and adaptability of open-source tools like Calliope for exploratory analysis. Future research could build on this model by incorporating varying electricity and hydrogen prices, simplified foreign demand profiles, or expanding the nearly optimal solution alternatives with a new post-processing tool, Modelling to Generate Continuous Alternatives.