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This Virtual Special Issue collects the results of six research projects conducted under the umbrella of the MODEX network. MODEX stands for Model Experiments and refers to the combination of qualitative and quantitative model comparisons, the latter based on a harmonized model application. While all projects focused on the comparison of energy system models, the experiments covered a broad range of different areas. Thus, the experiments address different model types, such as vehicle diffusion models, power system models, distribution grid models, and transmission grid models, but also different thematic scopes, such as policy instruments, decentralized flexibility, and sector coupling. In total, the projects involved 40 models. The experiments were based on a harmonization of models and data, which was realized to the degree possible and useful in the respective project context. Complementary to the harmonized model application, the methods and tools for the systematic comparison of energy system models were further developed within the MODEX projects. The results of the MODEX projects significantly expand the spectrum of systematic comparisons of energy system models. The added value compared to previous model experiments consists particularly in the profound characterization of the models involved, the consistent linking of model differences with outcome differences, and the transparent description of appropriate methods and challenges of comparing energy system models. Due to a high degree of transparency, the project findings can be used directly by developers and users of other models

In which way, and in which sectors, will renewable energy be integrated in the German Energy System by 2030, 2040, and 2050? How can the resulting energy system be characterised following a −95% greenhouse gas emission reduction scenario? Which role will hydrogen play? To address these research questions, techno-economic energy system modelling was performed. Evaluation of the resulting operation of energy technologies was carried out from a system and a business point of view. Special consideration of gas technologies, such as hydrogen production, transport, and storage, was taken as a large-scale and long-term energy storage option and key enabler for the decarbonisation of the non-electric sectors. The broad set of results gives insight into the entangled interactions of the future energy technology portfolio and its operation within a coupled energy system. Amongst other energy demands, CO2 emissions, hydrogen production, and future power plant capacities are presented. One main conclusion is that integrating the first elements of a large-scale hydrogen infrastructure into the German energy system, already, by 2030 is necessary for ensuring the supply of upscaling demands across all sectors. Within the regulatory regime of 2020, it seems that this decision may come too late, which jeopardises the achievement of transition targets within the horizon 2050.

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

To assess the resilience of energy systems, i.e., the ability to recover after an unexpected shock, the system’s minimum state of service is a key input. Quantitative descriptions of such states are inherently elusive. The measures adopted by governments to contain COVID-19 have provided empirical data, which may serve as a proxy for such states of minimum service. Here, we systematize the impact of the adopted COVID-19 measures on the electricity demand. We classify the measures into three phases of increasing stringency, ranging from working from home to soft and full lockdowns, for four major electricity consuming countries of Europe. We use readily accessible data from the European Network of Transmission System Operators for Electricity as a basis. For each country and phase, we derive representative daily load profiles with hourly resolution obtained by k-medoids clustering. The analysis could unravel the influence of the different measures to the energy consumption and the differences among the four countries. It is observed that the daily peak load is considerably flattened and the total electricity consumption decreases by up to 30% under the circumstances brought about by the COVID-19 restrictions. These demand profiles are useful for the energy planning community, especially when designing future electricity systems with a focus on system resilience and a more digitalised society in terms of working from home., German Federal Ministry of Economic Affairs and Energy, German Federal Ministry of Education and Research

Der vorliegende zweite Teil - Sektorenkopplung und Wasserstoff: Zwei Seiten der gleichen Medaille knüpft an diesem Punkt an. Er befasst sich detailliert mit möglichen Nutzungspfaden von Wasserstoff in den Sektoren Verkehr, Industrie und Wärme sowie mit dem systemisch wichtigen Aspekt der Rückverstromung. Am Beispiel aktueller Forschungsarbeiten im DLR wird dargestellt, welche Potenziale sich durch die Kopplung der verschiedenen Energieverbrauchssektoren bei der Erzeugung und Nutzung von Wasserstoff ergeben. Diese Synergien ebnen den Weg zu einer effizienteren und flexibleren Nutzung von erneuerbarer Energie. Neben den technologischen Einsatzmöglichkeiten spielen darüber hinaus die Infrastruktur sowie dazugehörige Sicherheitsaspekte bei der Nutzung von Wasserstoff eine gewichtige Rolle. Weiterhin wird im DLR auf dem Gebiet der Energiesystemanalyse an der Einschätzung der Auswirkungen großskaliger Wasserstoffinfrastrukturen auf das bestehende und zukünftige Energiesystem geforscht. Dies beinhaltet auch die Fragestellungen, ob ein klimaneutrales Energiesystem aus heutiger Sicht überhaupt ohne Wasserstoff denkbar ist, bzw. wie groß der Wasserstoffbedarf und das -angebot in solchen Zielszenarien ausfällt. Zudem werden Umwelteinwirkungen untersucht und Lebenszyklusanalysen erstellt.

Background Energy system models (ESM) are widely used in research and industry to analyze todays and future energy systems and potential pathways for the European energy transition. Current studies address future policy design, analysis of technology pathways and of future energy systems. To address these questions and support the transformation of today’s energy systems, ESM have to increase in complexity to provide valuable quantitative insights for policy makers and industry. Especially when dealing with uncertainty and in integrating large shares of renewable energies, ESM require a detailed implementation of the underlying electricity system. The increased complexity of the models makes the application of ESM more and more difficult, as the models are limited by the available computational power of today’s decentralized workstations. Severe simplifications of the models are common strategies to solve problems in a reasonable amount of time – naturally significantly influencing the validity of results and reliability of the models in general. Solutions for Energy-System Modelling Within BEAM-ME a consortium of researchers from different research fields (system analysis, mathematics, operations research and informatics) develop new strategies to increase the computational performance of energy system models and to transform energy system models for usage on high performance computing clusters. Within the project, an ESM will be applied on two of Germany’s fastest supercomputers. To further demonstrate the general application of named techniques on ESM, a model experiment is implemented as part of the project. Within this experiment up to six energy system models will jointly develop, implement and benchmark speed-up methods. Finally, continually collecting all experiences from the project and the experiment, identified efficient strategies will be documented and general standards for increasing computational performance and for applying ESM to high performance computing will be documented in a best-practice guide.

Battery electric vehicles (BEV) provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. Assumptions on fleet battery availability and a detailed representation of fast charging are found to have a strong impact on wind curtailment, renewable energy feed-in, and required gas power plant flexibility. A representation of fleet connection to the grid in high temporal detail is less important. Peak load can be reduced by 5% and 3% in both scenarios, respectively. Shifted load is robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

The expected increase in battery electric vehicles poses both, opportunities and risks for the decarbonisation of future power systems. While electric mobility may technically serve as a source of flexibility, it increases the overall power demand and may as well increase the demand for power in peak load hours. While electric mobility in national and continental scale energy system optimization is often treated in an aggregated fleet, user behaviour and charging decisions play a crucial role in the availability of vehicles' batteries for balancing electric load and renewable energy feed-in. Michaelis, Gnann & Klingler (2018) find that reduction of peak load and RE surplus electricity stemming from plug-in vehicles in Germany may be as high as 2.2 GW and 1.8 TWh respectively for 2030. However, they assume constant charging stations' power of 3.7 kW that seem conservative. Also, the authors don't explicitly describe to what effect user behaviour limits vehicle charging. By linking the modular energy system optimization model REMix with the vehicle stock model VECTOR21 and the charging decision simulation tool CURRENT, we aim to determine a more realistic demand response potential for future electric vehicles compared to previous studies. All three models are developed at the German Aerospace Center (DLR) and have been applied and validated in different project contexts. We for the first time integrate hourly profiles determined by individual charging decisions as input for an energy system optimization model. We determine the reduction potential of peak load, renewable energy (RE) curtailment as well as RE capacity expansion for the different cases of controlled charging versus uncontrolled charging for Germany for 2030. Thus, we provide both methodological and quantitative insights into the effects of coupling power and transport sector that are of value to the scientific discussion.

This paper presents a methodology for the development of normative energy scenarios. The focus is on cross-sectoral transformation pathways towards a completely renewable energy supply. By using the REMix energy model, which is temporal and spatially highly resolved, the least-cost power supply structure is assessed. The model also calculates additional storage and grid capacity necessary for the target year 2050. By analyzing the interactions of different technologies and sectors we provide a basis for the backcasting of a transformation pathway. This is carried out by a simulation model within the MESAP-PlaNet environment, covering the heat, transport & power sectors. We present and compare feasible transformation pathways towards a 100% renewable energy supply for two case studies, the Canary Islands and Brazil.

State-of-the-art energy system models include a comprehensive representation of energy sectors and their associated technologies while including high resolution spatial and temporal data. This complexity is increased further by technologies linking the system along temporal, spatial and sectoral dimensions such as electrical energy storages, transmission grids or combine heat and power plants. One of the downsides of the increasing detail in optimising energy system models is the necessary time to solve the model. The BEAM-ME project addresses the need for efficient solution strategies for the increasing complexity of energy system models and. The project is funded by the German Federal Ministry of Economic Affairs and Energy and brings together researchers from the fields of energy system analysis, mathematics, operations research and informatics. The goal of the project is to develop technical and conceptual strategies for every step of the solution process. This the formulation of the energy system model, improving the solvers and utilising the resources of high performance computing. This talk provides an overview of the challenges in adapting the formulation of energy systems in order to utilise specialised solvers which efficiently exploit the underlying block structures. This requires energy system modellers to annotate their models in order to communicate which parts of the model should be solved in parallel while still maintaining the ability to achieve a global optimum solution. Finally, first insights of current results are discussed and an outlook on subsequent steps is given.

Energy systems based on renewable resources are characterized by a high number of decentralized components as well as by an increasing importance of storage, grid, and demand response. State-of-the-art energy system models reflect this growing complexity by combining a comprehensive representation of energy sectors and technologies with a high spatial and temporal resolution. However, such high level of detail is typically associated with long solving times of the models. The reduction of the latter is urgently required and the need for guidelines for a reasonable balance between degree of detail and solution time is pressing. The BEAM-ME project addresses these questions by improved computing power and efficiency in energy systems modelling. The German Aerospace Center is the principal investigator on the modelling side, while the project gathers various partners with complementary expertise in the fields of optimization algorithms, high performance computing, and application development. This talk provides an overview and evaluation of conceptual and technical strategies identified so far to reduce the model solution time of the REMix energy system model. Moreover, an insight in the implementation and assessment of selected speed-up strategies is provided. Finally, conclusions from the first project results are drawn and an outlook on the subsequent works is given.

Greenhouse gas reduction is one of the main challenges toward obtaining a more sustainable energy system. The diffusion of electricity generation technologies that rely on renewable resources is one promising option to tackle this problem. However, the temporal mismatch of intermittent electricity generation (e.g. from wind power or photovoltaics) and load requires an increase in flexibility of the future energy system, for example provided by storage, dispatchable generation, or demand response. We quantify the potential contribution of an enhanced coupling between the electricity and heating sectors to balance intermittent renewable power generation in highly renewable energy scenarios for the U.S., focusing on the New England region (ISONE). For this purpose, a multi–sectoral approach is applied, combining the scenario construction methodology of the Stanford University with the cost optimizing capacity expansion and dispatch model REMix (Renewable Energy Mix). A particular focus lies on the flexible operation of Combined Heat and Power (CHP) plants and electric heat pumps (HP) enabled by thermal energy storage. Moreover, we evaluate the interaction and competition with alternative flexibility options, including power transmission, electric energy storage and controlled charging of battery electric vehicles (BEV).