Abstracts from the 9th DACH+ Conference on Energy Informatics
Optimal power flow algorithms can be used to optimally control power systems and to reduce need for grid expansions this way. However, optimization of power systems is a complex problem and still hardly possible in real-time, which would be necessary for grid control. In this doctoral project, a methodology is proposed to train artificial neural networks with the results from offline optimizations in order to speed-up calculation and to ensure feasibility of the optimization. That is expected to achieve fast and near-optimal results, but also allows for high modularity, which reduces engineering effort and makes the approach applicable to diverse use cases.