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

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

Článek WoS

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.

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.

smart grid; electricity network; flexibility assessment; renewable energy sources; machine learning; network simulation; artificial neural networks; convolutional neural networks

smart grid; electricity network; flexibility assessment; renewable energy sources; machine learning; network simulation; artificial neural networks; convolutional neural networks

KRČ, R.; KRATOCHVÍLOVÁ, M.; PODROUŽEK, J.; APELTAUER, T.; STUPKA, V.; PITNER, T.

2021

09.03.2021

MDPI

Basel, Switzerland

2071-1050

Sustainability

13

5

Švýcarská konfederace

1

18

18

https://www.mdpi.com/2071-1050/13/5/2954/pdf

http://hdl.handle.net/11012/200892

sustainability-13-02954