Publikationen

The energy transition in Germany demands a comprehensive transformation of electricity grids at all levels, making them central to the achievement of climate goals and the integration of renewable energy in all energy sectors. However, grid infrastructure currently represents a critical bottleneck. In particular, the role of medium and low voltage grids is changing. Historically designed to distribute electricity from the transmission system to demands, medium and low voltage grids must now adapt to accommodate distributed generation, increased flexibility, and new demands driven by sector coupling. Flexibility options, such as demand response and energy storage, offer potential solutions, but their efficient deployment requires optimized coordination. In order to contribute to the identification of an effective coordination strategy, the open-source tool eGo (electricity grid optimization) has been developed. The objective is to quantify the expansion needs of the grid at all levels in Germany, considering flexibility options from the sectors electricity, heat, gas, and mobility using a top–down approach. The method is applied to a mid-term scenario depicting the year 2035. The results show that while top–down dispatch optimization is effective for extra-high and high voltage grids, it can exacerbate expansion requirements in medium and low voltage grids. As a result, flexibility options can reduce system costs by only 1\%, taking into account the need for expansion at all grid levels. The curtailment of intermittent renewables can be significantly reduced by 71\%. Coordinated dispatch across grid operators and levels is therefore essential to fully leverage flexibility options, ensuring that the energy transition proceeds efficiently across the entire electricity grid.

Energy system modeling has been following the energy transition to investigate challenges and opportunities of future energy systems on all grid levels. Necessary input for sector-coupled energy system models are residential electricity and heat demand curves. The increasing importance of distribution grids and their modeling requires demand profiles in high spatial resolution.This paper presents a method to assign pre-generated electricity and heat demand curves to georeferenced residential buildings in Germany. We aim at overcoming fundamental shortcomings of the Standard Load Profiles and enable new possibilities for the modeling of distribution grids. Our approach provides a large variety in residential load profiles which spatially correspond to official socio-demographic data. All used input data sets as well as implemented methodology and the resulting profiles are publicly available under open source and open data licences to enable further use. Our results are validated on different aggregation levels as well as compared and discussed with the commonly used Standard Load Profiles.