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In a preparative study, the use of different sorbent materials for in situ desulfurization of biomass derived synthesis gas is evaluated. Results of phase equilibrium calculations using FactSage version 6.4 for the H 2 S equilibrium concentration with a variation of different parameters are compared to values for the residual H 2 S content found in literature. The focus is strictly set on a high temperature, high steam application for biomass-derived syngas conditions. Possible synergistic effects between different sorbent components and deviations from simulated results are discussed subsequently. The use of copper-based sorbent material turns out to be promising due to a predicted positive impact of high steam conditions for sorption equilibria. Results for the implementation of a CaO−BaO mixed phase sorbent are confirmed, qualifying this material combination as a promising candidate for in situ application. Equilibrium calculations predict a desulfurization of syngas to a sulfur level of 2.1 ppm v H 2 S at a steam content of about 40 vol % and 820 °C which is sufficient for further catalytic gas processing applications.

Background: The transition to a sustainable future challenges the current energy grids with the integration of variable, distributed renewable energy sources. On a technical level, multi-energy systems may provide the necessary flexibility to minimise the gap between demand and supply. Suitable methods and tools are necessary to derive relevant results and to support a transition to renewable energy sources. While several, dedicated tools to model grids and infrastructure of single-energy carriers exist, there are no tools capable of modelling multi-energy systems in detail. Thus, this paper presents the necessary aspects to consider when modelling grid-based multi-energy systems, presents three open source frameworks for modelling grid-based energy systems and points out the major challenges. Methodology: The current main aspects and challenges for modelling grid-based energy systems are derived from a literature review. Three open source multi-energy modelling frameworks (Calliope, oemof, urbs) are presented, and the extent to which they consider these aspects and how they tackle challenges is analysed. Grid-based MES modelling: We identified five general energy system modelling aspects (modelling scope, model formulation, spatial coverage, time horizon, data) and three aspects specific to modelling energy grids (level of detail, spatial resolution, temporal resolution). While the specific aspects mainly influence the representation of the technical parts of the energy system and the computational effort, the general aspects primarily relate to the system boundaries and scope of the model. For the evaluation of the modelling results, we identified several assessment criteria, including economic, energetic, exergetic and reliability. Each of the studied open source modelling frameworks provides generic capabilities to model energy converters, and the electricity, gas and district heat networks. However, the general and specific aspects present respective challenges. Relating to the general aspects, complexity of model formulation increases when including additional boundary conditions. The accuracy of the results is also dependent on data quality. Temporal and spatial resolutions are the major specific challenges for modelling the energy infrastructure. Conclusions: There is still a broad field of opportunities for researchers to contribute to grid-based energy system modelling. This encompasses especially the consideration of short-and long-term dynamics of renewable energy sources in planning models.

While an area-wide implementation of electric vehicles (EVs) and electric heat pumps (HPs) will contribute to a decarbonization of the energy system, they represent new challenges for existing low-voltage (LV) power grids. Hence, this study investigates potential grid congestions on the basis of three contrasting load approaches applied to four different grid regions. Within the three load approaches, temporal characteristics of various grid customer classes (EVs, HPs, households etc.) are derived from highly resolved realistic load profiles. In accordance with classic grid planning, firstly a static load approach is analyzed by applying the modeled coincidence for each consumer class individually. Secondly, this static approach is modified by including combined coincidence factors, taking temporal consumer class interactions into account. Finally, both static load approaches are compared with detailed annual time series analyses by means of load flow simulations using real-life LV grid data. The evaluation of inadmissible voltage characteristics and thermal congestions identifies future grid extension needs depending on the considered grid region. In addition, the variation of the applied load approach highlights the need to consider consumer-specific temporal behavior. In fact, by neglecting temporal interactions between conventional and future grid customers, the classic grid planning approach overestimates future grid extension needs. To counteract an oversizing of future grid structures, this paper presents a combined consideration of EVs’ and HPs’ coincidence as well as resulting grid consequences on the LV level.

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The energy transition from fossil-based energy sources to renewable energy sources of an industrialized country is a big challenge and needs major systemic changes to the energy supply. Such changes require a holistic view of the energy system, which includes both renewable potentials and consumption. Thereby exergy, which describes the quality of energy, must also be considered. In this work, the determination and analysis of such a holistic view of a country are presented, using Austria as an example. The methodology enables the calculation of the spatially resolved current exergy consumption, the spatially resolved current useful exergy demand and the spatially resolved technical potential of renewable energy sources (RES). Top-down and bottom-up approaches are combined in order to increase accuracy. We found that, currently, Austria cannot self-supply with exergy using only RES. Therefore, Austria should increase the efficiency of its energy system, since the overall exergy efficiency is only at 34%. The spatially resolved analysis shows that in Austria the exergy potential of RES is rather evenly distributed. In contrast, the exergy consumption is concentrated in urban and industrial areas. Therefore, the future energy infrastructure must compensate for these spatial discrepancies.

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HyFlow is a grid-based multi-energy system (MES) modelling framework. It aims tomodel the status quo of current energy systems, future scenarios with a high share of fluctuatingenergy sources or additional consumers like electric vehicles, and to compare solution strategies ifcertain parts of the infrastructure are congested. In order to evaluate the congestion limits and thefeasibility and suitability of solution strategies (e.g., energy storage, sector coupling technologies,demand response (DR)), load flow calculations of all three main grid-bound energy carriers areimplemented in one single modelling framework. In addition to the implemented load flow models,it allows the interaction of these grids with the use of hybrid elements. This measure enables aproper assessment of future scenarios, not only for the infrastructure of one energy carrier, but forthe overall energy system. The calculation workflow of HyFlow, including the implemented loadflow calculations, as well as the implementation of the flexibility options, is described in detail inthe methodology section. To demonstrate the wide range of applicability of HyFlow with differentspatial ranges, two case studies referring to current research problems are presented: a city and aregion surrounding the mentioned city. The calculations for the mentioned case studies areperformed for three levels. A “status quo” level, a “high-stress” level with added fluctuatingenergy sources and consumers, and an “improvement” level, where flexibility options areintroduced to the system. The effect of the flexibility options on future energy grids is, therefore,analyzed and evaluated. A wide variety of evaluation criteria can be selected. For example, themaximum load of certain power lines, the self-sufficiency of the overall system, the total transportlosses or the total energy consumption.