The European energy system faces one of its greatest challenges: transitioning from a system dominated by fossil-based energy sources to a completely climate neutral system in 2050. Energy system models provide useful tools that can help to navigate this complex task of designing a future energy system by modelling future systems and assessing the impact of future design choices.

Current literature adopts either a step-wise optimisation towards a final configuration of an energy system or deploys a modelling to generate alternatives (MGA) approach to generate a diverse set of system configurations. While the first approach provides temporal insights, it may biased and miss less trivial solutions, the second approach offers robustness but lacks insights into timing of technol- ogy deployments and neglects existing infrastructure. This highlights a gap in combining the strengths of both approaches to understand the dynamic between short-term decisions and long-term flexibility, while being robust under changing conditions.

The purpose of this study is to develop a method that combines the strengths of both modelling approaches. A method is developed and applied to two case studies to uncover how policy targets influence the maneuvering space towards a climate neutral European energy system. Many European nation states have set ambitious targets to increase renewable generation capacity while also phasing out fossil-based power generation. It is important to analyse impact of such targets on the rest of the energy system in the short- and the long-term. Aggressive phase-out or growth of deployed infrastructure might work in the short term but could restrict flexibility of options further into the future.

This study uses spatially explicit practically optimal results (SPORES) for 2030 and 2050 provided by the sector-coupled Euro-calliope model, which is an adaptation of the MGA approach. Energy system characteristics were found by analysis of the distributions of primary energy sources and power sector technology deployments. Trade-offs were uncovered by computing Pearson correlation of technology deployments on national and European scale. A k-means clustering algorithm was applied to condense the set of hundreds of SPORES to manageable amount of scenarios that is more accessible for poli- cymakers. The scenarios reveal trends and trade-offs between the two different time-frames. Finally, two case-studies were presented that use a filtering of the SPORES to reveal the impact of the 2030 PV capacity target in Germany and the 2030 offshore wind capacity target in the Netherlands on the maneuvering space of their respective future energy systems. Key finding of this study include:
• Phase-out of fossil-based energy sources by 2050 is enabled by a doubling of renewable elec- tricity generation by 2030 and a more than ten-fold increase of renewable electricity generation in 2050.
• Solar (PV) and wind turbines are must-have technologies by 2050, however trade-offs exist in the the proportional balance between them, and the timing and location of deployment.
• Early phase-out of coal-fired power generation can reduce flexibility in the system as it is often accompanied by high deployment of gas turbines and PV that could create lock-in risk.
• Germany’s ambitions to deploy 215 GW of PV by 2030 requires increased coal-fired generation capacity to remain in the cost-efficient design space of 2030. Thus the PV target introduces lock-in risk of coal-fired power generation, which requires a complete phase out by 2050.
• The Dutch offshore wind target of 21 GW by 2030 introduces a potential conflict between deploy- ment of offshore wind and growth of PV and onshore wind that is required for 2050.

These findings contribute to research by offering a methodology that improves understanding of the dynamics within the European energy transition. This study has, for the first time, placed MGA solutions in the context of the multi-decade transition. By analysing the change between the current system, the design space spanned by the 2030 and 2050 SPORES, new insights about the time dependent trade- offs within the energy system and the limitations of the SPORES method have emerged.