The transformation of the energy system from fossil to renewable energy sources requires considerable changes in the expansion of power plant, grid and energy storage. Energy system models are an important tool for assessing the consequences of political, technical or economic developments. As the level of detail (disaggregation) increases, the effort and complexity of the computing processes also increase. For each question, the degree of detail of the model must be weighed against the computational effort.
This is where the research project WeatherAggReOpt comes in: Together with the University of Duisburg-Essen, Fraunhofer ISE is working on methods to reduce the complexity of energy system models while minimizing the discrepancy of the results.
For this purpose, theoretical considerations on the optimal (dis-)aggregation within optimization models are applied. By using spatially and temporally high resolution weather information generation potentials are determined. Afterwards the feed-in profiles are analyzed to aggregate similar plants on the basis of similar site conditions. This enables a better representation of different generation profiles within the optimization model. In addition to the spatial resolution of the feed-in profiles, the spatial resolution of the electricity grid and the conventional power plants are also investigated. With the complexity of the power grids the computing costs can significantly increase. Here we are searching for methods that keep the computational effort low and still allow a high spatial resolution. With regard to time aggregation, the focus is primarily on reducing the time steps that are necessary to achieve meaningful and stable results.
In addition to the temporal and spatial aggregation, an aggregation of the power generation from renewable energy plants to meaningful technology classes is also to be carried out, which can then be selected substitutively or complementarily in the optimization. The study will also examine how energy storage can be adequately mapped. The aggregation approaches are to be developed in such a way that they can be implemented in different energy system models and thus the models achieve a higher performance.