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

Rostislav Krč, Martina Kratochvílová, Jan Podroužek, Tomáš Apeltauer, Václav Stupka and Tomáš Pitner
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Rostislav Krč: Institute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno University of Technology, 602 00 Brno, Czech Republic
Martina Kratochvílová: Institute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno University of Technology, 602 00 Brno, Czech Republic
Jan Podroužek: Institute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno University of Technology, 602 00 Brno, Czech Republic
Tomáš Apeltauer: Institute of Computer Aided Engineering and Computer Science, Faculty of Civil Engineering, Brno University of Technology, 602 00 Brno, Czech Republic
Václav Stupka: Centre for Education, Research and Innovation in Information and Communication Technologies-ExecUnit, Faculty of Informatics, Masaryk University, 602 00 Brno, Czech Republic
Tomáš Pitner: Centre for Education, Research and Innovation in Information and Communication Technologies-ExecUnit, Faculty of Informatics, Masaryk University, 602 00 Brno, Czech Republic

Sustainability, 2021, vol. 13, issue 5, 1-18

Abstract: 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.

Keywords: smart grid; electricity network; flexibility assessment; renewable energy sources; machine learning; network simulation; artificial neural networks; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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