  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.
