Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization

The decarbonization of energy systems has led to a fundamental change in their topologysince generation is shifted to locations with favorable renewable conditions. In planning, this changeis reflected by applying optimization models to regions within a country to optimize the distributionof generation units and to evaluate the resulting impact on the grid topology. This paper proposesa globally applicable framework to find a suitable regionalization for energy system models witha data-driven approach. Based on a global, spatially resolved database of demand, generation,and renewable profiles, hierarchical clustering with fine-tuning is performed. This regionalizationapproach is applied by modeling the resulting regions in an optimization model including asynthesized grid. In an exemplary case study, South Africa’s energy system is examined. The resultsshow that the data-driven regionalization is beneficial compared to the common approach of usingpolitical regions. Furthermore, the results of a modeled 80% decarbonization until 2045 demonstratethat the integration of renewable energy sources fundamentally changes the role of regions withinSouth Africa’s energy system. Thereby, the electricity exchange between regions is also impacted,leading to a different grid topology. Using clustered regions improves the understanding and analysisof regional transformations in the decarbonization process

Towards A Modular IT-Landscape For Manufacturing Companies: Framework For Holistic Software Modularization

Companies in the manufacturing sector are confronted with an increasingly dynamic environment. Thus, corporate processes and, consequently, the supporting IT landscape must change. This need is not yet fully met in the development of information systems. While best-of-breed approaches are available, monolithic systems that no longer meet the manufacturing industry's requirements are still prevalent in practical use. A modular structure of IT landscapes could combine the advantages of individual and standard information systems and meet the need for adaptability. At present, however, there is no established standard for the modular design of IT landscapes in the field of manufacturing companies' information systems. This paper presents different ways of the modular design of IT landscapes and information systems and analyzes their objects of modularization. For this purpose, a systematic literature research is carried out in the subject area of software and modularization. Starting from the V-model as a reference model, a framework for different levels of modularization was developed by identifying that most scientific approaches carry out modularization at the data structure-based and source code-based levels. Only a few sources address the consideration of modularization at the level of the software environment-based and software function-based level. In particular, no domain-specific application of these levels of modularization, e.g., for manufacturing, was identified

Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization

The decarbonization of energy systems has led to a fundamental change in their topology since generation is shifted to locations with favorable renewable conditions. In planning, this change is reflected by applying optimization models to regions within a country to optimize the distribution of generation units and to evaluate the resulting impact on the grid topology. This paper proposes a globally applicable framework to find a suitable regionalization for energy system models with a data-driven approach. Based on a global, spatially resolved database of demand, generation, and renewable profiles, hierarchical clustering with fine-tuning is performed. This regionalization approach is applied by modeling the resulting regions in an optimization model including a synthesized grid. In an exemplary case study, South Africa’s energy system is examined. The results show that the data-driven regionalization is beneficial compared to the common approach of using political regions. Furthermore, the results of a modeled 80% decarbonization until 2045 demonstrate that the integration of renewable energy sources fundamentally changes the role of regions within South Africa’s energy system. Thereby, the electricity exchange between regions is also impacted, leading to a different grid topology. Using clustered regions improves the understanding and analysis of regional transformations in the decarbonization process

Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization

The decarbonization of energy systems has led to a fundamental change in their topologysince generation is shifted to locations with favorable renewable conditions. In planning, this changeis reflected by applying optimization models to regions within a country to optimize the distributionof generation units and to evaluate the resulting impact on the grid topology. This paper proposesa globally applicable framework to find a suitable regionalization for energy system models witha data-driven approach. Based on a global, spatially resolved database of demand, generation,and renewable profiles, hierarchical clustering with fine-tuning is performed. This regionalizationapproach is applied by modeling the resulting regions in an optimization model including asynthesized grid. In an exemplary case study, South Africa’s energy system is examined. The resultsshow that the data-driven regionalization is beneficial compared to the common approach of usingpolitical regions. Furthermore, the results of a modeled 80% decarbonization until 2045 demonstratethat the integration of renewable energy sources fundamentally changes the role of regions withinSouth Africa’s energy system. Thereby, the electricity exchange between regions is also impacted,leading to a different grid topology. Using clustered regions improves the understanding and analysisof regional transformations in the decarbonization process