Graph Modeling for Efficient Retrieval of Power Network Model Change History

Ivana Dalčeković, Aleksandar Erdeljan, Nikola Dalčeković and Jelena Marjanović
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Ivana Dalčeković: Faculty of Technical Sciences, Department of Power, Electronic and Telecommunication Engineering, University of Novi Sad, Trg D. Obradovića 6, Novi Sad 21000, Serbia
Aleksandar Erdeljan: Faculty of Technical Sciences, Department of Computing and Control Engineering, University of Novi Sad, Trg D. Obradovića 6, Novi Sad 21000, Serbia
Nikola Dalčeković: Faculty of Technical Sciences, Department of Power, Electronic and Telecommunication Engineering, University of Novi Sad, Trg D. Obradovića 6, Novi Sad 21000, Serbia
Jelena Marjanović: Faculty of Technical Sciences, Department of Power, Electronic and Telecommunication Engineering, University of Novi Sad, Trg D. Obradovića 6, Novi Sad 21000, Serbia

Energies, 2021, vol. 14, issue 24, 1-19

Abstract: Power grids are constantly evolving, and data changes are increasing. Operational technology (OT) is controlled by IT technologies in smart grids, where changes in the physical world impose changes in the software data model, as well as the continuous generation of data points, resulting in time series datasets. The increased need for processing large amounts of data combined with requirements to maintain and increase overall performances has created a significant challenge for traditional database solutions and relational database models. The main idea of this paper was to find and propose a graph model that will allow the retrieval of historical connectivity in a reduced time complexity. Furthermore, the research question was addressed by evaluating three different approaches where the results provide a foundation for the proposed design guidelines related to optimizing graph-based databases for a modern smart grid system. The results of the experiments demonstrated reduced time complexities from 3 to 5 times depending on the typical industry usage patterns and the selected graph model. This suggests that the design decision may severely affect the outcome for given smart grid use cases when using historical features in OT technologies. Therefore, the main contribution of the research is the proposed guidelines on how to design an optimal graph model that satisfies the described smart grid requirements.

Keywords: graph database; history; smart grids (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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