• Energy Research

Pathways leading to a carbon neutral future for the German energy system have to deal with the expected phase-out of coal-fired power generation, in addition to the shutdown of nuclear power plants and the rapid ramp-up of photovoltaics and wind power generation. An analysis of the expected impact on electricity market, security of supply, and system stability must consider the European context because of the strong coupling—both from an economic and a system operation point of view—through the cross-border power exchange of Germany with its neighbors. This analysis, complemented by options to improve the existing development plans, is the purpose of this paper. We propose a multilevel energy system modeling, including electricity market, network congestion management, and system stability, to identify challenges for the years 2023 and 2035. Out of the results, we would like to highlight the positive role of innovative combined heat and power (CHP) solutions securing power and heat supply, the importance of a network congestion management utilizing flexibility from sector coupling, and the essential network extension plans. Network congestion and reduced security margins will become the new normal. We conclude that future energy systems require expanded flexibilities in combination with forward planning of operation.

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.