Europe and North America account for 32 % of current carbon emissions. Due to distinct legacy systems, energy infrastructure, socioeconomic development, and energy resource endowment, both regions have different policy and technological pathways to reach net zero by the mid-century. Against this background, our paper examines the results from the net zero emission scenarios for Europe and North America that emerged from the collaboration of the European and American Energy Modeling Forums. In our analysis, we perform an inter-comparison of various integrated assessments and bottom-up energy system models. A clear qualitative consensus emerges on five main points. First, Europe and the United States reach net zero targets with electrification, demand-side reductions, and carbon capture and sequestration technologies. Second, the use of carbon capture and sequestration is more predominant in the United States due to a steeper decarbonization schedule. Third, the buildings sector is the easiest to electrify in both regions. Fourth, the industrial sector is the hardest to electrify in the United States and transportation in Europe. Fifth, in both regions, the transition in the energy mix is driven by the substitution of coal and natural gas with solar and wind, but to a different extent.

Background

Article 12 of the Paris Agreement summons the signing parties to co-operate in improving the education of their citizens on climate change and related matters. The article thereby acknowledges the importance of citizens’ support and understanding of climate change and needed measures to fight climate change. This work aims to inform European citizens on how climate change-related policies affect the power sector in Europe. For this purpose, a serious game, based on sound principles of energy systems analysis, has been developed to allow players to explore how key policy decisions affect capacity mix, investment needs, and electricity costs.

Results

The game is based on more than 1700 scenarios run through an open-source and accessible, yet technologically detailed, myopic energy system optimisation model for the electricity supply in the EU27 + 3. The game allows the user to take the role of a decision-maker and make decisions in 2020, 2030, and 2040 regarding the usage of CCS, biomass imports, cross-border electricity transmission and the pace of emission reductions. The user is then presented with economic, social, and environmental impacts of these choices. These impacts are, for example, measured and illustrated in the development of accumulated CO2 emissions per capita, levelised cost of electricity, and investment need per citizen.

Conclusion

The Power Decisions Game provides a first-of-its-kind open-source infrastructure that allows non-modellers to explore the impact of key decisions and preferences on the design of the future European power system. Furthermore, it provides insights on the consequences of short-sighted decision making. The game can be used to facilitate policy-science discussions.

 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.

The transition to a low carbon energy system as laid out in the Paris Agreement and the European GreenDeal presents challenges that involve society at all levels from planners to consumers. A key challenge isthe communication across these levels. Tools to foster engagement and discussion between the differentactors are open-source models with a low threshold for uptake. This paper presents the Open-Sourceelectricity Model Base for Europe an electricity sector engagement model covering all member statesof the EU, Norway, Switzerland and the United Kingdom. Built in OSeMOSYS and available on GitHub, themodel provides a starting point into energy systems modelling and can be further developed in acollaborative manner. It enables non-experts to develop an understanding of energy systems models andenergy planning. Thereby, it can serve as an engagement tool to carry the debate on the future of theEuropean power system beyond the academy, which might contribute to finding societal consensus onhow to decarbonise our energy system. The model allows dynamic power sector expansion analysis ofthe European power system till 2050. It can be used for scenario analysis and is expandable to othersectors to analyse the benefits of sector coupling.

This paper presents a long-term open source energy planning model for Denmark, Estonia, Finland, Latvia, Lithuania, Poland, and Sweden, as part of the preparation of a Pan-European model within the Horizon 2020 REEEM project. The model is built using the Open Source Energy Modelling System (OSeMOSYS) and is conceived as a stakeholder engagement model, comprehensive but accessible. It aims to lower the threshold to join and contribute to a model-based discussion about the optimal decarbonisation pathways for the energy supply of the region. The lowest net present value for the modelled system and period (2015-2050) is calculated by using linear optimization. Existing and planned trans-border transmission capacity between the included countries is considered in the model. New projects are also allowed as far as economically optimal. The electricity exchangeto countries not covered by the model are not modelled as of yet. Ten fuels are used by the technologies defined in the model, namely biomass, coal, geothermal, heavy fuel oil, hydro, natural gas, nuclear, wind and waste. In addition to technology parameters like investment cost, fuel cost, and fixed and variable operation and maintenance cost, an increasing emission penalty for carbon dioxide is defined, which represents the cost related to the emission of greenhouse gases (similar to the European emission trading system). The model provides insights on how the cross-border electricity exchange might develop in the modelled period while decarbonizing the energy sector and considering the unequal distribution of (renewable) resources. But most importantly, the model builds the base for the first fully open source energy model for Europe, including the used data. It shall be conceived as a comprehensive modular tool for engagement in the field of European energy planning, especially for learning in academia, but also by the integration into an open engagement game for decision makers and stakeholders.

A common challenge in energy system optimisation models is the representationof investment decision patterns. Such capacity expansion models often assume perfectforesight. Thereby they assume certainty for the entire modelling horizon. Thisproduces investment patterns that are cost optimal on the long-term but not necessarilyfor single assets on the short-term. Therefore, models with limited foresight can providevaluable insights when trying to anticipate investment patterns in the power sector.This article presents a Python package that expands the capabilities of the OpenSource energy Modelling System (OSeMOSYS) to facilitate modelling with limitedforesight. Furthermore, it applies the package to investigate the impact of foresighthorizon length on the accumulated CO2 emissions in a decarbonisation scenario andinvestigates the possibilities to model disruptive events.To expand the functionality of OSeMOSYS, a fully open Python package isdeveloped that allows the user to run a provided model with limited foresight. Themodel time horizon is divided into multiple independently cost-optimised time stepswith the option to vary parameters between time steps to represent decisions ordisruptive events. The package allows to run decision trees by providing multipledata options for model parameters. The decision tree then incorporates all possiblecombinations of the provided options.The article shows for the case of a decarbonisation scenario that a shortening of theforesight horizon leads to an increase in CO2 emissions accumulated over the modellinghorizon. This illustrates the importance of analysing and considering foresight horizonlengths at different levels of decision making in policy design.The analysis of model results when applying the decision tree feature illustratethat OSeMOSYS step facilitates to model sudden and disruptive events while keepinglimited foresight. This allows for analysis of the robustness of the energy system todeal with sudden changes in the boundary conditions.