To achieve climate goals, it is necessary to decarbonise the transport sector, which requires an immediate changeover to alternative power sources (e.g., battery powered vehicles). This change will lead to an increase in the demand for electrical energy, which will cause additional stress on power grids. It is therefore necessary to evaluate energy and power requirements of a future society using e-mobility. Therefore, we present a new approach to investigate the influence of increasing e-mobility on a distribution grid level. This includes the development of a power grid model based on a cellular approach, reducing computation efforts, and allowing time and spatially resolved grid stress analysis based on different load and renewable energy source scenarios. The results show that by using the simplified grid model at least seven times, more scenarios can be calculated in the same time. In addition, we demonstrate the capability of this novel approach by analysing the influence of different penetrations of e-mobility on the grid load using a case study, which is calculated using synthetic charging load profiles based on a real-life mobility data. The results from this case study show an increase on line utilisations with increasing e-mobility and the influence of producers at the same connection point as e-mobility.

A long-term sustainable energy transition can only be achieved by technological advancements and new approaches for efficiently integrating renewable energies into the overall energy system. Significantly increasing the share of renewable energy sources (RES) within the overall energy system requires appropriate network models of current transmission and distribution grids, which, as limiting factors of energy infrastructures, confine this share due to capacity constraints. However, especially regarding electrical network models, data (e.g., geographical data, load and generation profiles, etc.) is rarely available since it usually includes user-specific information and is, therefore, subject to data protection. Synthetically obtained electrical networks, on the other hand, may not be representative and may fail to replicate real grid structures due to the heterogeneous properties of currently operated networks. To account for this heterogeneity, this paper offers a contribution for the European electrical energy system and presents the development of four synthetic test networks at different voltage levels which are representative and include non-confidential time-series data. The test network development is based on an extensive literature research on a multitude of different network parameters for grids within the ENTSO-E (European Network of Transmission System Operators for Electricity) interconnected system in Europe. These parameters are then used to design the networks in NEPLAN®. Then, these networks are provided with load and generation profiles for enabling time-series calculations. To validate the representativeness of the test networks, a short-circuit analysis is conducted and the obtained results are compared to short-circuit parameters common for Austrian and German literature values as well as for value ranges for European ENTSO-E grids. The analysis shows that the presented test networks replicate European electrical network behavior accurately and can, therefore, be utilized for various application purposes to assess technological impacts on European ENTSO-E grids.

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The high emission intensity of coal-fired power plants (CFPP) leads to the inevitable next step towards energy transition, the coal phase-out. One challenge is the subsequent use of still-functioning assets. Re-purposing these assets avoids value loss and creates new opportunities for coal regions. Therefore, this study considers the sector coupling technologies Power-to-Gas (PtG) and Gas-to-Power (GtP) as re-purposing options. First, a multi-variable Mixed-Integer Linear Programming optimisation model is established. This model includes the participation of the plant in the current (2020) and future (2030, 2040) electricity and natural gas spot-markets and the balancing power market while fulfilling existing contracts, and allows for determining the re-purposing technologies' operating profiles. By applying a techno-economic analysis, investment recovery periods of the considered re-purposing technologies are assessed, which range between two (GtP) and over ten (PtG) years. A sensitivity analysis accounting for current energy prices and technological advancements reveals capital expenditure has the highest impact on this Return-On-Investment period. Additionally, a case study considering the Austrian energy grids is performed to account for the grid impact of integrating these technologies at former CFPP sites. Thus, it is found that the investigated sector coupling technologies have the potential to compensate for grid congestions even in profit-optimised operation. K E Y W O R D Scoal phase-out, combined cycle gas turbine, energy markets, mixed-integer linear programming, power-to-gas | INTRODUCTIONCoal represents the most common fossil fuel resource and is the largest source for electricity generation, providing about 37% of the global electricity demand [1]. Additionally, coal electricity generation is considered reliable and cost-effective [2]. However, since coal is also the most carbon-intensive fossil fuel, currently accounting for more than 30% of global CO 2 emissions [3], coal-fired electricity generation is under political and economic pressure [2]. Therefore, the coal phaseout [4] is an inevitable next step for European countries to rapidly achieve climate neutrality of the electrical energy system [4]. To remain within the carbon budget of the Paris Agreement [5], 72% of the CFPPs operating in 2020 within the European Union (EU) have to be shut down by 2025 [4, 6]. While more than 30% of the European countries already have coal-free power generation, the coal phase-out is not even under discussion in 24% of the European countries. A more detailed description of the coal phase-out trends in Europe can be seen in Figure 1. According to the individual national phaseout plans, 63% of the European countries are expected to generate coal-free electricity by 2030 [7].The coal phase-out, however, leads to the early closure of existing coal-fired power plant (coal-fired power plants (CFPP)) sites before they reach end-of-life. Thus, the coal phase-out entails the risk of leaving valuable assets (e.g. infr...