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.

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The European Union and the Austrian government have set ambitious plans to expand renewable energy sources and lower carbon dioxide emissions. However, the expansion of volatile renewable energy sources may affect today’s energy system. To investigate future challenges in Austria’s energy system, a suitable simulation methodology, i.e., temporal and spatially resolved generation and consumption data and energy grid depiction, is necessary. In this paper, we introduce a flexible multi-energy simulation framework with optimization capabilities that can be applied to a broad range of use cases. Furthermore, it is shown how a spatially and temporally resolved multi-energy system model can be set up on a national scale. To consider actual infrastructure properties, a detailed energy grid depiction is considered. Three scenarios assess the potential future energy system of Austria, focusing on the power grid, based on the government’s renewable energy sources expansion targets in the year 2030. Results show that the overwhelming majority of line overloads accrue in Austria’s power distribution grid. Furthermore, the mode of operation of flexible consumer and generation also affects the number of line overloads as well.

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...