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• W2984642011 abstract "The energy transition entails a rapid uptake of renewable energy sources. Besides physical changes within the grid infrastructure, energy storage devices and their smart operation are key measures to master the resulting challenges like, e.g., a highly fluctuating power generation. For the latter, optimization based control has demonstrated its potential on a microgrid level. However, if a network of coupled microgrids is considered, iterative optimization schemes including several communication rounds are typically used. Here, we propose to replace the optimization on the microgrid level by using surrogate models either derived from radial basis functions or neural networks to avoid this iterative procedure. We prove well-posedness of our approach and demonstrate its efficiency by numerical simulations based on real data provided by an Australian grid operator." @default.
• W2984642011 created "2019-11-22" @default.
• W2984642011 creator A5070856778 @default.
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• W2984642011 date "2019-11-11" @default.
• W2984642011 modified "2023-10-03" @default.
• W2984642011 title "Surrogate Models in Bidirectional Optimization of Coupled Microgrids." @default.
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• W2984642011 hasPublicationYear "2019" @default.
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