Impact evaluation of cyber-physical uncertainty on power systems

This thesis evaluates the impact on power system performance of uncertainty arising from physical and cyber components of power systems. If the impact of the uncertainty arising from emerging technologies is not fully understood, it will likely lead to the deployment of unreliable and unsafe systems, which could have catastrophic consequences. In this thesis, we first identify sources of uncertainty, and their impact on both system dynamic security and relia- bility. Then, we recognize the gaps that have not been studied in the existing related work, and develop models and methods to fill the gaps. We mainly focus on the evaluation of uncertainty impact in two applica- tions: (i) the impact of measurement uncertainty on power system dynamic performance (at the transmission level), and (ii) the impact of uncertain phenomena on power systems coordinating demand response resources (at distribution level). With respect to the first application, we focus on the impact of both measurement errors and delays on the dynamic performance of power systems with automatic generation control (AGC). A framework to model the deterministic and random measurement errors, and measure- ment delays, as well as the corresponding analysis methods, are developed. Along the process, the different time scales of the system dynamics, as well as the discrete nature of the sampling process in AGC, should be considered. Eventually, with the developed framework, we can determine system stability under various measurement uncertainty scenarios. In the second application, we have developed a stochastic hybrid system (SHS) model that can capture both continuous dynamics and discrete events that arise from random fail- ures and repairs. A reliability measure is also proposed and evaluated. In order to illustrate and validate the proposed evaluation methods proposed in this thesis, the results of all proposed analytical methods addressing the random factors are compared with those obtained by Monte Carlo methods via examples and case studies.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2016

Author dc:creator

• Zhang, Jiangmeng

Contributors dc:contributor

• Domínguez-García, Alejandro
• Sauer, Peter W.
• Overbye, Thomas J.
• DeVille, Lee

• Power system uncertainty
• Impact Evaluation
• Cyber-physical security
• Demand response system reliability

Statement dc:rights

• Copyright 2016 Jiangmeng Zhang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/92828

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/92828