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Data management improvements in the electrical grid

a pathway to a smarter cyber-physical system

Time: Mon 2024-04-08 10.00

Location: F3 (Flodis), Lindstedtsvägen 26

Subject area: Electrical Engineering

Doctoral student: Sylvie Evelyne Koziel , Elektromagnetism och fusionsfysik

Opponent: Assistant Professor Mathaios Panteli, University of Cyprus, Nicosia, Cyprus

Supervisor: Professor Patrik Hilber, Elektromagnetism och fusionsfysik; Doctor Ebrahim Shayesteh, Elektromagnetism och fusionsfysik; Associate Senior Lecturer Per Westerlund,

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QC 20240321

Abstract

The current power system is a network of electrical components forming a physical system. It is experiencing changes, such as the deployment of electric vehicles and distributed energy sources. Meanwhile, cybernetworks are becoming coupled into the physical grid to an increasing degree. This transformation of electrical grids into smart grids is often the focus of research literature. It is strongly linked to the concurrent evolution of the cyberinfrastructure, which is the focus of this thesis. It aims to support the incremental upgrade of data systems, taking real-world constraints into account, as well as opportunities offered by sensors and machine learning. With this background, two research questions have been identified: i) How to use available data more efficiently to improve asset management? and ii) How much and which kind of new data are actually needed?

With regard to the first research question, ways to use available data more efficiently are investigated in collaboration with a distribution system operator (DSO). One option is for DSOs to adopt best practices in terms of data management, which include: de-silo data, enhance reporting practices, and automatize tasks. To illustrate how combining available data may deliver additional relevant information, criticality indices have been calculated and assigned to components of a substation, by combining outage data, operation data and network diagram. Another option is to develop machine learning algorithms to perform specific or new tasks. A failure warning system has been developed using machine learning to leverage existing data about power components. It provides component-specific red flags, in situations where the widespread installation of component-specific sensors is unrealistic.

With regard to the second research question, a methodology has been developed to identify which data are actually needed. The approach is scenario-based, and formalizes mathematically the relations between data, grid management and grid performance. It has been applied in three studies. One study uses the methodology to explore impacts of data quality on grid management costs, and to evaluate the most profitable amount of investment needed into data quality improvement. Another study provides a tool to evaluate the profitability of investments in condition-monitoring sensors. A third study investigates how data granularity affects decisions in grid upgrades, and ultimately the quality of power supply, and proposes a way to decide where, how, and how much to upgrade the cyberinfrastructure.

In summary, this thesis: i) shows the importance of data for grid performance; ii) conceptualizes, formalizes mathematically, and quantifies relations between data, grid management, and grid performance; iii) develops new approaches to support the transformation of the cyberinfrastructure needed for a transition to smart grids.

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