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.

The aim of this thesis project is to investigate how different PV-deployment strategies can help reinforce energy security and reduce GHG emissions in Ukraine. The author addresses several sub-questions related to energy security, the current status of Ukraine's electricity and heat sector, and the potentials and yields of renewable energy sources in Ukraine. To answer this question, the author uses literature review and simulations using optimization software. Three different deployment strategies are considered: utility-scale PV systems, distributed PV systems, and a combination of both. The simulations are based on hourly data for solar irradiance, temperature, wind speed, and load demand for 2018 in Ukraine. The results are evaluated based on several key performance indicators (KPIs) linked to energy security such as import dependency, diversity, as well as GHG emissions reduction potential.

The author finds that a combination of utility-scale and distributed PV systems can provide the most effective solution for reinforcing energy security while also reducing GHG emissions in Ukraine. The simulations also show that improvements in grid infrastructure is crucial for achieving these results.
Overall, the author concludes that deploying both utility-scale and distributed PV systems can provide a cost-effective way to reinforce energy security while also reducing GHG emissions in Ukraine.

Currently, there are optimization models that are able of modeling building renovation, but their scope is limited only to the heating system. There are also models that consider interactions between different energy subsystems, but they lack the ability to simulate building renovation. Having those two things combined would be beneficial as the impact of renovation on the power system might be significant.
The goal of this thesis is to assess the impact of building renovation on the energy system. It was done with the use of Euro-calliope model, which is capable of minimizing the cost of the whole energy system. In the current state, Euro-calliope model does not offer the chance to renovate the building stock. Therefore, the aim of this work is to introduce the building renovation option subject to the software objective function and reshape, when necessary, the heating sector.
The main outcome is that the heating sector significantly affects the distribution of power generation sources as most of heat is supplied via heat pumps. The penetration of renovation increases the fraction of energy generated by photovoltaics in the energy mix. However, when it comes to absolute values, in all scenarios wind farms are dominating.
The cost-optimal renovation always results in a higher renovation level than the currently present renovation levels. However, those levels are usually lower than the currently imposed local renovation standards. Moreover, the renovation has a positive impact on decreasing the variability of the system costs for scenarios with low renewable supply.