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.