This research aims to investigate the potential impact of national policies on the attainment of Europe's goal of achieving net-zero greenhouse gas emissions by 2050. Specifically, it analyses the effects of policies on the power sector, by evaluating capacity expansion portfolios, import reliance, and costs by 2050. A linear programming model, the IESA-EUPS, is utilized to optimize the expansion and operation of the power system, considering 28 nodes and hourly temporal resolution. The study includes five scenarios from 2020 to 2050, with varying levels of biomass and nuclear penetration based on existing member state policies. Results show that by 2050, changes mainly occur in the interplay between firm capacities and cross-border transmission levels. Limiting biomass can significantly increase nuclear energy generation, while enforcing all policies leads to a 40 % rise in cross-border transmission by 2050, due to imbalances between countries. Some member states, such as Spain and Finland, are less affected, whereas others are heavily reliant on firm nuclear capacities. Western European countries with strict biomass and nuclear restrictions may see a boost in nuclear installations in countries allowing it. Member states without both nuclear and biomass may rely more on variable renewables, resulting in surplus electricity and increased LCOE.

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Background: The transition to a climate neutral society such as that envisaged in the European Union Green Deal requires careful and comprehensive planning. Integrated assessment models (IAMs) and energy system optimisation models (ESOMs) are both commonly used for policy advice and in the process of policy design. In Europe, a vast landscape of these models has emerged and both kinds of models have been part of numerous model comparison and model linking exercises. However, IAMs and ESOMs have rarely been compared or linked with one another. Methods: This study conducts an explorative comparison and identifies possible flows of information between 11 of the integrated assessment and energy system models in the European Climate and Energy Modelling Forum. The study identifies and compares regional aggregations and commonly reported variables. We define harmonised regions and a subset of shared result variables that enable the comparison of scenario results across the models. Results: The results highlight how power generation and demand development are related and driven by regional and sectoral drivers. They also show that demand developments like for hydrogen can be linked with power generation potentials such as onshore wind power. Lastly, the results show that the role of nuclear power is related to the availability of wind resources. Conclusions: This comparison and analysis of modelling results across model type boundaries provides modellers and policymakers with a better understanding of how to interpret both IAM and ESOM results. It also highlights the need for community standards for region definitions and information about reported variables to facilitate future comparisons of this kind. The comparison shows that regional aggregations might conceal differences within regions that are potentially of interest for national policy makers thereby indicating a need for national-level analysis.

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Energy models are used to study emissions mitigation pathways, such as those compatible with the Paris Agreement goals. These models vary in structure, objectives, parameterization and level of detail, yielding differences in the computed energy and climate policy scenarios. To study model differences, diagnostic indicators are common practice in many academic fields, for example, in the physical climate sciences. However, they have not yet been applied systematically in mitigation literature, beyond addressing individual model dimensions. Here we address this gap by quantifying energy model typology along five dimensions: responsiveness, mitigation strategies, energy supply, energy demand and mitigation costs and effort, each expressed through several diagnostic indicators. The framework is applied to a diagnostic experiment with eight energy models in which we explore ten scenarios focusing on Europe. Comparing indicators to the ensemble yields comprehensive ‘energy model fingerprints’, which describe systematic model behaviour and contextualize model differences for future multi-model comparison studies.@en