Replacing Distributed Optimization by Surrogate Models in Coupled Microgrids

The rapid uptake of renewable energy sources and energy storage devices are characteristic traits of the energy transition and require a transformation of the grid from unito bidirectional transmission. In addition to the implementation of the necessary physical changes within the grid, also sophisticated decision making plays a major role to make use of inherent flexibilities. To this end, optimization based control has demonstrated its potential on a microgrid level. In this paper, we consider a network of coupled microgrids. Using optimization necessitates several communication and optimization steps within the grid. We construct surrogate models by using Radial Basis Functions or Neural Networks to avoid this step. We prove the efficiency of the proposed methodology numerically based on real-data provided by an Australian distribution grid operator.

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