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Description: In recent years, European gas transport has been affected by major disruptive events like political issues such as, most recently, the Russian war on Ukraine. To incorporate the impacts of such events into decision-making during the energy transition, more complex models for gas network analysis are required. However, the limited availability of consistent data presents a significant obstacle in this endeavor. We use a mathematical-modeling-based scenario generator to deal with this obstacle. The scenario generator consists of capacitated network flow models representing the gas network at different aggregation levels. In this study, we present the coarse-to-fine approach utilized in this scenario generator.

Description: Most recently, the European energy system has undergone a fundamental transformation to meet decarbonization targets without compromising the security of the energy supply. The transition involves several energy-generating and consuming sectors emphasizing sector coupling. The increase in the share of renewable energy sources has revealed the need for flexibility in supporting the electricity grid to cope with the resulting high degree of uncertainty. The new technologies accompanying the energy system transition and the recent political crisis in Europe threatening the security of the energy supply have invalidated the experience from the past by drastically changing the conventional scenarios. Hence, supporting strategic planning tools with detailed operational energy network models with appropriate mathematical precision has become more important than ever to understand the impacts of these disruptive changes. In this paper, we propose a workflow to investigate optimal energy transition pathways considering sector coupling. This workflow involves an integrated operational analysis of the electricity market, its transmission grid, and the gas grid in high spatio-temporal resolution. Thus, the workflow enables decision-makers to evaluate the reliability of high-level models even in case of disruptive events. We demonstrate the capabilities of the proposed workflow using results from a pan-European case study. The case study, spanning 2020-2050, illustrates that feasible potential pathways to carbon neutrality are heavily influenced by political and technological constraints. Through integrated operational analysis, we identify scenarios where strategic decisions become costly or infeasible given the existing electricity and gas networks.

Description: The decarbonization of the European energy system demands a rapid and comprehensive transformation while securing energy supplies at all times. Still, natural gas plays a crucial role in this process. Recent unexpected events forced drastic changes in gas routes throughout Europe. Therefore, operational-level analysis of the gas transport networks and technical capacities to cope with these transitions using unconventional scenarios has become essential. Unfortunately, data limitations often hinder such analyses. To overcome this challenge, we propose a mathematical model-based scenario generator that enables operational analysis of the European gas network using open data. Our approach focuses on the consistent analysis of specific partitions of the gas transport network, whose network topology data is readily available. We generate reproducible and consistent node-based gas in/out-flow scenarios for these defined network partitions to enable feasibility analysis and data quality assessment. Our proposed method is demonstrated through several applications that address the feasibility analysis and data quality assessment of the German gas transport network. By using open data and a mathematical modeling approach, our method allows for a more comprehensive understanding of the gas transport network's behavior and assists in decision-making during the transition to decarbonization.

Description: The investigation of energy transition paths toward a sustainable and decarbonized future under uncertainty is a critical aspect of contemporary energy planning and policy development. There are numerous methods for analysing uncertainties and sensitivities and many studies on sustainable transformation paths, but there is a lack of combined application to relevant use-cases. In this study, we investigate the sensitivity of energy transition paths to uncertainties in operational and investment costs of power plants in the metropolitan area of Berlin and its rural surroundings. By employing the linear programming energy system model oemof-B3, we extensively focus on the system's energy technologies, such as wind turbines, photovoltaics, hydro and combustion plants, and energy storages. Greenhouse gas reduction and electrification rates per commodity are realized by selected constraints. Our research aims to discern how investments in energy production capacities are influenced by uncertainties of other energy technologies' investment and operational costs in the system. We apply a quantitative approach to investigate such interdependencies of cost variations and their impact on long-term energy planning. Thus, the analysis sheds light on the robustness of energy transition paths in the face of these uncertainties. The region Berlin-Brandenburg serves as a case study and thus reflects on the present space conflicts to meet energy demands in urban and suburban areas and their rural surroundings. An electricity-intensive scenario is selected that assumes a 100 % reduction in greenhouse gas emissions by 2050. With the results of the case study, we show how our approach enables rural and metropolitan decision-makers to collaborate in achieving sustainable energy. Decision-making in long-term energy planning can be made more robust and flexible by acknowledging the identified sensitivities and enable such regions better to navigate challenges and uncertainties associated with sustainable energy planning.