@inproceedings{wol20,
Author = {Wolgast, Thomas},
Title = {Real-Time Capable Optimal Power Flow With Artificial Neural Networks},

Year = {2020},
Publisher = {Springer},
Booktitle = {Abstracts from the 9th DACH+ Conference on Energy Informatics},
Doi = {10.1186/s42162-020-00113-9},
Url = {https://energyinformatics.springeropen.com/articles/10.1186/s42162-020-00113-9#Sec48},
type = {inproceedings},
Abstract = {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.}
}
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