Towards the Modeling of Flexibility Using Artificial Neural Networks in Energy Management and Smart Grids

Modeling flexibility using artificial neural networks

“…As mentioned before, this paper is based on the ideas outlined in (Förderer et al 2018). Due to the variety of possible applications for the concept and patterns, the presented results are related to a multitude of previous publications.…”

“…In contrast to these, in the concept presented in (Förderer et al 2018) and evaluated in this paper the ANN is generated locally but used externally by a third party.…”

“…The patterns outlined in this section are a generalization of the five usage patterns for ANN-encoded abstracted flexibility presented in (Förderer et al 2018). We omit pattern E which allows the optimization and communication of load changes rather than absolute load profiles, since it is basically an extension of the other patterns.…”

“…Feasible in the context of DER load profiles refers to load profiles that can be realized based on the current state while providing all necessary services (Mauser et al 2017a). In this paper we pick up the idea of representing and communicating flexibility with artificial neural networks (ANNs) presented in (Förderer et al 2018): A single ANN implicitly learns a flexibility model for one or multiple aggregated DERs using generated or measured load profiles and state data relating to the corresponding DERs as training data. It is important to note that a single (measured) load profile does generally give only few clues about the available flexibility.…”

“…This paper aspires to evaluate the idea of using ANNs as surrogate models for flexibility by testing the effectiveness of different ANN types and topologies in conjunction with the patterns presented in (Förderer et al 2018). By explaining the particularly good or bad results achieved for a given pattern and DER configuration, we aid future research in designing better ANNs that represent energy flexibility.…”