Doctoral Dissertations

Orcid ID

Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Electrical Engineering

Major Professor

Dr. Fangxing Li

Committee Members

Dr. Kevin Tomsovic, Dr. Hector Pulgar, Dr. Mingzhou Jin


The COVID-19 pandemic has forced many companies and business to operate through remote platforms, which has made everyday life and everyone more digitally connected than ever before. The cybersecurity has become a bigger priority in all aspects of life. A few real-world cases have demonstrated the current capability of cyberattacks as in [1], [2], and [3]. These cases invalidate the traditional belief that cyberattacks are unable to penetrate real-world industrial systems. Beyond the physical damage, some attackers target financial arbitrage advantages brought by false data injection attacks (FDIAs) [4]. Malicious breaches into power market operations could induce catastrophic consequences on fair financial settlements and reliable transmission services. In this dissertation, an in-depth study is conducted to investigate power market cybersecurity and profit-targeting cyberattacks. In the first work, we demonstrate the importance of market-level behavior in defending cyberattacks and designing cyberattacks. A market-level defense analysis is developed to help operators identify cyberattacks, and an LMP-disguising attack strategy is developed to disguise the abnormal LMPs, which can bypass both the bad data detection and market-level detection. In the second work, we propose a comprehensive CVA model for delivering a detailed analysis of four aspects of vulnerability: highly probable cyberattack targets, devastating attack targets, risky load levels, and mitigation ability under different degrees of defense. In the third work, we identify that revenue adequacy, a fundamental power market operation criterion, has not been analyzed under the context of cybersecurity, and we explore the impact of FDIAs targeting real-time (RT) market operations on ISO revenue adequacy analytically and numerically. In the last work, we extend the power system cybersecurity analysis to multi-energy system (MES) framework. An optimally coordinated (OC-FDIA) targeting MES is proposed. Then, we show that the OC-FDIA cause much more severe damages than single-system FDIA and uncoordinated FDIAs. Further, an effective countermeasure is developed against the proposed OCFDIA based on deep learning technique (DL).

Files over 3MB may be slow to open. For best results, right-click and select "save as..."