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Abstract
Future smart grids heavily rely on collaboration of two interdependent subsystems: the information and communication (ICT) system and the electrical system itself. The past has shown that malfunctions in one of the systems can propagate into the other system leading to widespread and heavy outages of power supply. Within this PhD project, a methodology for modeling, assessing and quantifying the Smart Grids’ robustness against occurrence of important unpredictable events is developed. Special attention is paid to the interdependencies between ICT and electrical subsystem and a particular novelty to existing approaches is the robustness quantification based on the current state of both subsystems allowing constant evaluation.
Highlights
Motivation The information- and communication (ICT) subsystem and the electrical subsystem of modern power systems are interdependent
In order to address the overall target, this PhD project is subdivided into the following research questions: RQ1: How is the robustness of a distribution system influenced by incidents happening in the electrical subsystem or the information and communication (ICT) subsystem?
It is envisioned to quantify the robustness of a Smart Grid using and combining particular models for both, the ICT subsystem and the electrical subsystem of the grid
Summary
Motivation The information- and communication (ICT) subsystem and the electrical subsystem of modern power systems are interdependent. Research questions The top-level research question of this work is: How can machine learning methods applied to assess (typecast and quantify) interdependencies of distribution systems and corresponding ICT systems during operation in order to derive statements to the overall system robustness? In order to address the overall target, this PhD project is subdivided into the following research questions: RQ1: How is the robustness of a distribution system influenced by incidents happening in the electrical subsystem or the ICT subsystem?
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