Enhanced power grid evaluation through efficient stochastic model-based analysis

TL;DR

This paper discusses advanced stochastic model-based analysis techniques for evaluating Smart Grid reliability and performance, especially under cyber threats, by proposing new modeling approaches and studying grid dynamics.

Contribution

It introduces a modular, compositional modeling approach for Smart Grids and explores improved electrical grid dynamics representation in the context of cyber threats.

Findings

Enhanced modeling strategies for Smart Grid analysis.

Insights into grid behavior under cyber faults and attacks.

Proposed modular modeling framework for dependability assessment.

Abstract

Electrical infrastructures provide services at the basis of a number of application sectors, several of which are critical from the perspective of human life, environment or financials. Following the increasing trend in electricity generation from renewable sources, pushed by the need to meet sustainable energy goals in many countries, more sophisticated control strategies are being adopted to regulate the operation of the electric power system, driving electrical infrastructures towards the so called Smart Grid scenario. It is therefore paramount to be assisted by technologies able to analyze the Smart Grid behavior in critical scenarios, e.g. where cyber malfunctions or grid disruptions occur. In this context, stochastic model-based analysis are well suited to assess dependability and quality of service related indicators, and continuous improvements in modeling strategies and system models design are required. Thus, my PhD work addresses this topic by contributing to study new Smart Grid scenarios, concerning the advanced interplay between ICT and electrical infrastructures in presence of cyber faults/attacks, define a new modeling approach, based on modularity and composition, and start to study how to improve the electrical grid dynamics representation. In this article these studies are briefly presented and discussed.