False Data Injection Attack and Corresponding Countermeasure in Multienergy Systems

Abstract

Worldwide ambitions to combat climate change have expedited the penetration of renewables and decarbonization. Multienergy systems (MES), where different energy systems are optimally coordinated, have been recognized as a key element in future low-carbon energy operations. However, MES operations require intense exchanges of information and control signals, which inevitably intensify the risk of cyberattacks. Existing cybersecurity research mainly focuses on a single-energy system, with only a few pioneering cybersecurity analyses for MES focusing on uncoordinated cyberattacks. A lack of detailed discussion and analysis on the optimally coordinated cyberattack targeting MES prevents operators from accurately evaluating the potential damages of cyberattack in MES operations. Therefore, this paper first proposes an optimally coordinated false data injection attack (OC-FDIA) against MES, where attacks from different energy systems are coordinated to disturb the MES operation. Then, we show that the OC-FDIA can cause synergetic effects leading to much more severe damage than single-system FDIAs and uncoordinated FDIAs. Further, an effective countermeasure is developed to mitigate the OC-FDIA based on deep learning (DL). Eventually, the proposed OC-FDIA and its countermeasures are demonstrated through integrated electricity and gas test systems.