Data-Driven Modeling of Decarbonization Pathways for Worldwide Energy Systems Based on Archetypes and Spatial Clustering Methods

Energy systems worldwide face transitions by the integration of renewable generation sources which are a major driver to reduce carbon emissions and fulfill decarbonization targets. To quantify mid- and long-term effects of these transitions, energy system optimization models are applied. These models can support the design of efficient policies and the development of suitable technologies. However, modeling each country individually requires computational power, sufficient data, and human processing time for the validation and result evaluation. This thesis provides an efficient framework for the comparison of countries’ decarbonization pathways. Instead of increasing the computational power with brute force, the key principal is a data-driven approach accelerating the preparation and evaluation of energy system models. For this purpose, clustering algorithms are applied in two hierarcical stages: the first stage summarizes countries in global energy system archetypes to assess transitions for a reduced number of prototypical countries. The second stage clusters spatially highly resolved data to generate suitable regions for a multi-region model considering the differing spatial potentials of renewable generation. Both approaches are validated by three exemplary use cases: 1) grid topology, 2) green hydrogen production, and 3) coal phase-out. The developed approach is based on a global data basis combining socio-economic, geographic, and energy data including highly resolved geospatial data. This data basis is required for both clustering algorithms. The archetype clustering uses an extended k-Means algorithm. In the second stage, Ward’s method is implemented to cluster the spatial data. To avoid the curse of dimensionality by the high spatio-temporal resolution of hourly renewable generation profiles, Dynamic Time Warping and Principal Component Analysis reduce the time dimension in the clustering. For the first and third use case, the framework processes OpenStreetMap data to synthesize the existing grid structure and identify coal mining areas. The hydrogen use case uses a break-even price approach. The countries are classified in 15 archetypes, which is in a similar range as the definition of subregions by the United Nations. Compared to these regions, the classification in archetypes considers countries on different continents and thereby represents the energy system characteristics on average 30% better. Modeling an 80% decarbonization scenario between 2015 and 2045 for all archetypes shows the fundamental challenge that huge investments in currently less developed countries are needed. Furthermore, the modeling results confirm that the decarbonization pathway of countries is 10-30% closer to countries within the same archetype than to countries within the same geographic region. The use cases apply the developed framework from archetype clustering to regional clustering. For all three use cases, the archetypes lead to a selection of countries with suitable characteristis. Regarding the grid, Denmark, representing a country of a high renewable share, foresees fewer changes in its grid topology than Morocco. Second, Saudi Arabia is highly attractive for green hydrogen production with a low break-even price of 1€/kg in the context of a decarbonized system. Last, coal-dominated regions in South Africa face significant challenges since a coal phase-out shifts the generation to other regions with good renewable conditions. This thesis contributes to the worldwide application of energy system models which are important to determine cost-optimal decarbonized energy systems. The implementation of data-driven clustering methods simplifies and accelerates the modeling of each country globally. Thereby, modelers can use the data analysis as a first indication and focus on the model or its evaluation, policymakers can compare countries, or even regions within countries, and technology companies are able to quickly assess markets for their products