Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment

As energy distribution systems evolve from a traditional hierarchical load structure towards distributed smart grids, flexibility is increasingly investigated as both a key measure and core challenge of grid balancing. This paper contributes to the theoretical framework for quantifying network flexibility potential by introducing a machine learning based node characterization. In particular, artificial neural networks are considered for classification of historic demand data from several network substations. Performance of the resulting classifiers is evaluated with respect to clustering analysis and parameter space of the models considered, while the bootstrapping based statistical evaluation is reported in terms of mean confusion matrices. The resulting meta-models of individual nodes can be further utilized on a network level to mitigate the difficulties associated with identifying, implementing and actuating many small sources of energy flexibility, compared to the few large ones traditionally acknowledged.

Automated summary

This paper explores quantifying network flexibility potential using machine learning techniques, particularly artificial neural networks for classifying historic demand data. Performance of resulting classifiers is evaluated through clustering analysis and parameter space assessment, with statistical evaluation using bootstrapping to report mean confusion matrices.