The focus of the paper is on scenario studies that examine energy systems. This type of studies is usually based on formal energy models, from which energy policy recommendations are derived. In order to be valuable for strategic decision-making, the comprehensibility of these complex scenario studies is necessary. We aim at highlighting and mitigating the problematic issue of lacking transparency in such model-based scenario studies. In the first part of the paper, the important concept of transparency in the context of energy scenarios is introduced. In the second part, we develop transparency criteria based on expert judgement. The set of selected criteria is structured into ‘General Information’, ‘Empirical Data’, ‘Assumptions’, ‘Modeling’, ‘Results’, and ‘Conclusions and Recommendations’. Based on these criteria, a transparency checklist is generated. The proposed transparency checklist is not intended to measure the quality of energy scenario studies, but to deliver a tool which enables authors of energy scenario studies to increase the level of transparency of their work. The checklist thus serves as a standardized communication protocol and offers guidance for interpreting these studies. A reduced and a full version of the checklist are provided. The former simply lists the transparency criteria and can be adopted by authors with ease; the latter provides details on each criterion. We also illustrate how the transparency checklist may be applied by means of examples. We argue that transparency is a necessary condition for a reproducible and credible scenario study. Many energy scenario studies are at present characterized by an insufficient level of transparency. In essence, the checklist represents a synthesizing tool for improving their transparency. The target group of this work is experts, in their role of authors and/or readers of energy scenario studies. By applying the transparency checklist, the authors of energy scenario studies signal their commitment to a high degree of transparency, in consonance with scientific standards.

REMix is a framework for modeling energy systems of almost any temporal, spatial, and technological scale and detail. It can be used to model processes of extraction, storage, conversion, transfer and demand for any commodity, usually representing energy carriers such as electricity, gases, or heat, and to flexibly link them with each other. All processes can be characterized by indicators such as costs or CO 2 emissions. The mathematical core of the framework allows integer and continuous decision variables and the objective function includes one or more indicators. Energy system infrastructures can be considered for both economic dispatch and capacity expansion planning along either single target years or full infrastructure pathways. Research subjects are usually regional to continental systems, and their transformation to a climate-neutral energy supply.

Energy system optimization models used for capacity expansion and dispatch planning are established tools for decision-making support in both energy industry and energy politics. The ever-increasing complexity of the systems under consideration leads to an increase in mathematical problem size of the models. This implies limitations of today’s common solution approaches especially with regard to required computing times. To tackle this challenge many model-based speed-up approaches exist which, however, are typically only demonstrated on small generic test cases. In addition, in applied energy systems analysis the effects of such approaches are often not well understood. The novelty of this study is the systematic evaluation of several model reduction and heuristic decomposition techniques for a large applied energy system model using real data and particularly focusing on reachable speed-up. The applied model is typically used for examining German energy scenarios and allows expansion of storage and electricity transmission capacities. We find that initial computing times of more than two days can be reduced up to a factor of ten while having acceptable loss of accuracy. Moreover, we explain what we mean by “effectiveness of model reduction” which limits the possible speed-up with shared memory computers used in this study.

Energy scenario analyses are able to provide insights into the future and possible strategies for coping with challenges such as the integration of renewable energy sources. The models used for analyzing and developing future energy systems must be simplified, e.g., due to computational constraints. Therefore, grid-related effects and regional differences are often ignored. We tackle this issue by presenting a new methodology for aggregating spatially highly resolved transmission grid information for energy system models. In particular, such approaches are required in studies that evaluate the demand for spatially balancing power generation and consumption in future energy systems. Electricity transmission between regions is crucial, especially for scenarios that rely on high shares of renewable energy sources. The presented methodology estimates transmission line congestions by evaluating the nodal price differences and then applies a spectral clustering on these particular link attributes. The objective of the proposed approach is to derive aggregated model instances that preserve information regarding electricity transmission bottlenecks. The resulting models are evaluated against observables such as the annual amount of redispatched power generation. For a selection of defined performance indicators, we find a significantly higher accuracy compared to the commonly used, spatially aggregated models applied in the field of energy scenario analysis.