In this paper, we investigate the effect of distributed flexibilities on the operation of the transmission grid. The flexibilities considered are heat pumps, electric vehicles, battery energy storage systems and flexible renewable generation. For this purpose, we develop a two-stage approach of first determining an optimal electricity market solution considering the optimal dispatch of each generation element and flexibility. In the second step we determine the required dispatch adjustments due to transmission grid constraints and investigate the effect of integrating battery energy storage systems into the adjustable generators to solve congestions. In our case study, we investigate the central European transmission grid for a scenario based on the Distributed Generation scenario of the Ten-Year Network Development Plan for the year 2030. Integrating distributed flexibilities leads to a strong increase in the security of supply, while the overall effect on the generation adjustment is small. A comparison of the results for an AC and DC formulation shows that both approaches differ significantly in individual cases.

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Synthetic Natural Gas (SNG) is the most researched option for a Power-to-Fuel pathway in Germany after hydrogen, having the advantage of being compatible with the existing infrastructure. However, it is not clear under which conditions SNG is economically and environmentally advantageous compared to natural gas usage, since this is determined by a complex interplay of many factors. This study analyzes the technical, economic and environmental aspects of a pilot SNG plant to determine the key parameters for profitable and sustainable operation. The SNG plant was simulated in Aspen Plus® with CO2 from biogas production as a feedstock and with hydrogen provided by a 1 MWel electrolyzer unit. A life cycle analysis (LCA) was undertaken considering several impact categories with a special focus on global warming potential (GWP). An SNG cost of 0.33–4.22 €/kWhth was calculated, depending on factors such as operational hours, electricity price and type of electrolyzer. It was found that the CO2 price has a negligible effect on the SNG cost, while the electricity is the main cost driver. This shows that significant cost reductions will be needed for SNG to be competitive with natural gas. For the investigated scenarios, a CO2 tax of at least 1442 €/t was determined, calling for more drastic measures. Considering the global warming potential, only an operation with an emission factor of electricity below 121 g CO2-eq/kWhel leads to a reduction in emissions. This demonstrates that unless renewable energies are implemented at a much higher rate than predicted, no sustainable SNG production before 2050 will be possible in Germany.

The German energy transition has led to a strong expansion of renewable energies in recent years. As a result, the German population is increasingly coming into contact with generation facilities. To increase local acceptance for new installations and to create new sales channels for energy suppliers, the legislature has established the "System for Guarantees of Regional Origin" in 2019, which allows the marketing of electricity from subsidized facilities as "electricity generated in the region". However, regional electricity comes with additional costs on the procurement and sales side of energy suppliers, and it is unclear whether and to what extent consumers are willing to pay a premium for electricity generated regionally. This study investigates the willingness to pay (WTP) of residential customers based on two samples of 838 and 59 respondents, respectively. Our model results show that, on average, WTP for regional electricity generation is positive, especially among female, younger and bettereducated customers, although differences in WTP between these sociodemographic characteristics are small. Factors that are more relevant are the current type of electricity tariff, differentiated into non-green and green, with the latter having a positive influence, but also the tariff switching behavior of the past, which is a proxy for price sensitivity. Although WTP is positive, it is severely limited, and only pertains to a subgroup of electricity customers. Hence, it is not surprising that our simulation shows that including a regional green electricity tariff in an energy supplier's portfolio is likely to lead to product cannibalization, meaning that mainly green electricity customers will choose this tariff. From an energy supplier's perspective, these results raise the question of whether offering a regional electricity tariff is economically viable. Future research could further investigate what underlying factors drive preferences for regionally generated electricity and how it can contribute to local acceptance.

Models simulating household energy demand based on different occupant and household types and their behavioral patterns have received increasing attention over the last years due the need to better understand fundamental characteristics that shape the demand side. Most of the models described in the literature are based on Time Use Survey data and Markov chains. Due to the nature of the underlying data and the Markov property, it is not sufficiently possible to consider long-term dependencies over several days in occupant behavior. An accurate mapping of longterm dependencies in behavior is of increasing importance, e.g. for the determination of flexibility potentials of individual households urgently needed to compensate supplyside fluctuations of renewable based energy systems. The aim of this study is to bridge the gap between social practice theory, energy related activity modelling and novel machine learning approaches. The weaknesses of existing approaches are addressed by combining time use survey data with mobility data, which provide information about individual mobility behavior over periods of one week. In social practice theory, emphasis is placed on the sequencing and repetition of practices over time. This suggests that practices have a memory. Transformer models based on the attention mechanism and Long short-term memory (LSTM) based neural networks define the state of the art in the field of natural language processing (NLP) and are for the first time introduced in this paper for the generation of weekly activity profiles. In a first step an autoregressive model is presented, which generates synthetic weekly mobility schedules of individual occupants and thereby captures long-term dependencies in mobility behavior. In a second step, an imputation model enriches the weekly mobility schedules with detailed information about energy relevant at home activities. The weekly activity profiles build the basis for multiple use cases one of which is modelling consistent electricity, heat and mobility demand profiles of households. The approach developed provides the basis for making high-quality weekly activity data available to the general public without having to carry out complex application procedures.