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Project plan4res (www.plan4res.eu) involves the development of a modular framework for the modeling and analysis of energy system strategies at the European level. It will include models describing the investment and operation decisions for a wide variety of technologies related to electricity and non-electricity energy sectors across generation, consumption, transmission and distribution. The modularity of the framework allows for detailed modelling of major areas of energy systems that can help stakeholders from different backgrounds to focus on specific topics related to the energy landscape in Europe and to receive relevant outputs and insights tailored to their needs. The current paper presents a qualitative description of key concepts and methods of the novel modular optimization framework and provides insights into the corresponding energy landscape.
The German high-pressure natural gas transport network consists of thousands of interconnected elements spread over more than 120,000 km of pipelines built during the last 100 years. During the last decade, we have spent many person-years to extract consistent data out of the available sources, both public and private. Based on two case studies, we present some of the challenges we encountered.
Preparing consistent, high-quality data is surprisingly hard, and the effort necessary can hardly be overestimated. Thus, it is particularly important to decide which strategy regarding data curation to adopt. Which precision of the data is necessary? When is it more efficient to work with data that is just sufficiently correct on average?
In the case studies we describe our experiences and the strategies we adopted to deal with the obstacles and to minimize future effort.
Finally, we would like to emphasize that well-compiled data sets, publicly available for research purposes, provide the grounds for building innovative algorithmic solutions to the challenges of the future.
This report presents a method to integrate highly flexible technology models for distributed energy resources such as electric vehicles, power-to-heat systems, or home battery systems into a Lagrangian relaxation of the pan-European day-ahead electricity market (EULR). These flexible technology models are highly sensitive to the changes of Lagrangian multipliers within the iterative Lagrangian relaxation process, leading to volatile behavior. Furthermore, they show a high concurrency in their market behavior due to their technical homogeneity. Therefore, the method proposed in this report is an extension of the existing EULR modeling approach to improve the model's convergence. The methodological extension comprises a convex combination of iteration solutions for the Lagrangian relaxation subproblems similar to Dantzig-Wolfe decomposition. An exemplary case study shows the effectiveness of this extended approach.
The European energy system has been through a fundamental transformation since the Paris Agreement to reduce greenhouse gas emissions. 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 the electri
city grid. Thus, holistic planning of pathways towards decarbonized energy systems also involves assessing the gas infrastructure to provide such a flexibility and support for the security of supply. In this paper, we propose a workflow to investigate such 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. In a case study on a pan-European scale between 2020-2050, we show that carbon neutrality can be reached within feasible additional costs and in time. However, the manifestation of the potential pathways strongly depends on political and technological constraints. Sector coupling acts as an enabler of cross-border cooperation to achieve both, decarbonization and security of supply.
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.