Emission Pathways Towards a Low-Carbon Energy System for Europe: A Model-Based Analysis of Decarbonization Scenarios.

AuthorHainsch, Karlo

    One of the biggest contributors of greenhouse gas (GHG) emissions is the energy sector, accounting for more than two thirds of the global emissions (IEA, 2018). On a global scale, GHG emissions in the energy and industrial sector amounted to about 39 gigatonnes of C[O.sub.2] equivalent in 2017 (IEA, 2018), a number which has to be lowered substantially if climate targets are meant to be met (IPCC, 2018). Therefore, various challenges arise for different countries when it comes to decarbonize their energy systems. The European Union (EU), being a major economic force, has set several climate goal targets, which should lead to an energy system with almost no GHG emissions (European Comission, 2018). Yet, no exact confguration of the energy system is defined, and countries have to promote their own policies to reach the goals. As a consequence, these policies are not necessarily elaborated with one common European goal in mind but rather conficting national ones.

    In recent years, the focus was heavily set on decarbonizing the electricity sector (Gerbaulet et al., 2019; Child et al., 2019; Hansen et al., 2019; Jenkins et al., 2018). A high degree of electrification in these sectors is predicted in future scenarios, which implicitly affects the power sector. Since the current penetration of renewables in the electricity sector differs substantially between European countries, different challenges arise. While some countries need to tackle a complete overhaul of their mainly on fossil fuels relying electricity sector, others face the effects of increasing demands or limited potentials for renewable energies (European Comission, 2018). Moreover, the European Union faces the particular challenge of having declared climate targets for the whole Union but the economic development of the individual member states as well as the regions affected by structural change in the energy sector must be taken into account.

    Therefore, this paper analyzes the impact of different emission allocation methods on the European energy system and its transition towards less carbon intensity, with a special emphasis on the interaction between the different sectors. In the next chapter, the current state of the art of energy system modeling is highlighted. A brief overview of the Global Energy System Model (GENeSYS-MOD) and relevant data input is provided, followed by the scenario definition and emission allocation methods. Afterwards, the different results are shown and discussed and a brief conclusion is drawn. A preliminary version of this work was also published as a DIW Discussion Paper (Hainsch et al., 2018)


    The feld of energy system modeling experienced a substantial increase in interest over the last decades. Recent political debates, coupled with the required advances in computational power, lead to numerous studies and papers. Yet, traditionally, the power sector is by far the most widespread sector of choice when it comes to analyzing energy system transitions towards less GHG emissions. Several studies focus on the European electricity sector and analyze impacts of high renewable penetration (Scholz, 2012; PwC, 2011; Czisch, 2007; Plessmann and Blechinger, 2016). Scholz (2012) and Czisch (2007) come to similar results regarding the technical and economic feasibility of such electricity sectors. Plessmann and Blechinger (2016) suggest that such a system, which would meet the 2050 EU emission reduction target, can be achieved with investments of 403 billion Euro (EUR), increasing levelized cost of electricity (LCOE) by about 35%. These models provide excellent temporal and spatial resolution, which allow for a more detailed analysis of the electricity sector than what energy system models can achieve. However, the effects of other sectors are defined exogenous and, thus, no interactions are modeled. Moreover, examples show that lowering the temporal resolution does not necessarily impact the results significantly (Welsch et al., 2012; Blanford et al., 2018).

    Energy system models incorporate two or more sectors at the same time. On the one hand, this gain in complexity results in a loss of accuracy and often requires more assumptions to be made. On the other hand, it acknowledges the energy system as whole and highlights sector coupling. As GENeSYS-MOD can be classified as a bottom up model (Lofer et al., 2017), we will highlight the most commonly used models of this kind and outline the differences.

    Generally, the most common used bottom up models belong to the MARKet ALlocation (MARKAL) and the Model for Energy Supply Alternatives and their General Environmental Impacts (MESSAGE) family of models (Subramanian et al., 2018). MARKAL and its su ccessor The Integrated MARKAL-EFOM System (TIMES) were developed as part of the IEA-ETSAP (Energy Technology System Analysis Program) and newer versions combine a technical and economic approach. Similarly, MESSAGE links, through different modules, technological and macroeconomic developments to gain insights into future energy systems. This linkage, however, leads to a significant increase in complexity, which leads to lower spatial or temporal resolution compared to GENeSYS-MOD. Moreover, data and/or source codes are not necessarily publicly available.

    On a European scale, the PRIMES framework is the predominant one, being used also by the European Commission for impact assessment and analysis of policy options. The framework was used in the EUCO27 and EUCO30 policy scenarios on the basis of the EU Reference Scenario 2016. In their latest report, the European Comission (2018) uses the PRIMES framework to address pathways for the European energy system in line with the 2[degrees]C and 1.5[degrees]C scenarios. While the 2[degrees] C scenario aims for 80% GHG reduction compared to 1990, full decarbonization is required for keeping global mean temperature below 1.5[degrees]C. Moreover, an high amount of sector coupling leads to an increased electricity demand across all scenarios, which in some cases surpasses double of the current demand. However, the study does not assess the question of how to allocate the emission to the different member states. In addition, while the PRIMES framework is very rich in technological detail and combines it with behavioral modeling which follows micro-economic foundations (E3M-Lab, 2018), a general point of criticism is the lack of transparency, both in the formulation of the scenarios and the underlying assumptions used to compute the results.

    The earlier mentioned TIMES framework is also used in several studies which address the European energy system or parts of it. Nijs et al. (2018) conclude that either a combination of carbon capture and storage (CCS) or nuclear power or large amounts of renewable generation technologies are required to fulfll climate targets. In addition, they highlight the role of electrofuels in the context of sector coupling for those sectors without easy options of electrification. Others improved the representation of single sectors, like the residential (Chiodi et al., 2017) or transportation sector (Thiel et al., 2016), have certain regional focuses (Zeyringer et al., 2013), or use the framework to perform sensitivity analyses on common assumption (Nijs et al., 2015). Other work in the European context includes a study by Ram et al. (2018), proving that a European energy system which is based on 100% renewable energies is feasible, considering all energy sectors. Electricity demand increases by factor 4, yet the system cost are not higher than for the current confguration of the European energy system. As a downside, the model does not optimize inter-temporally, but rather optimizes each period individually.

    As shown, various modeling frameworks have been developed in recent time to analyze energy systems. Their ability of assisting in the development of policies and long term planning of energy systems are highlighted by their increasing usage from institutions and researchers. A lot of work has been done in the European context, driven by the European Commission and researchers, in showcasing how the energy transition towards less GHG emissions could develop. However, the studies generally do not deal with the question of how different emission allocation methods affect the single countries. Moreover, the used frameworks are mostly nontransparent in their underlying model and scenario assumptions. Therefore, this work aims to bridge that research gap, providing an analysis of the feasibility of different emission distribution and climate scenarios, while providing a fully open source code. The underlying research question is what the underlying implications of different carbon constraints are on a future low-cost energy system in Europe.


    3.1 General model description

    The model for analyzing the research questions is based on the formulation of the Global Energy System Model (GENeSYS-MOD), as described by Lofer et al. (2017) and Burandt et al. (2018).

    In essence, GENeSYS-MOD can be illustrated as a flow-based inter-temporal cost-optimization model. The different nodes are represented as Technologies, which are connected by Fuels. Examples for Technologies are production entities like wind or solar power, conversion technologies like heat pumps, storages, or vehicles. Fuels serve as connections between these technologies and can be interpreted as the arcs of the network. In general, Fuels represent energy carriers like electricity or fossil fuels, but also more abstract units like demands of a specific energy carrier or areas of land are classified as Fuels. Also, Technologies might require multiple different Fuels or can have more than one output fuel. As an example, a combined heat and power plant could use coal as an input fuel and produce electricity and heating energy as an output fuel. Eficiencies of the technologies are being accounted for in this exact...

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