Cyberattack detection methods for battery energy storage systems
Nina Kharlamova, Chresten Træhold, Seyedmostafa Hashemi
Abstract
Battery energy storage systems (BESSs) play a key role in the renewable energy transition. Meanwhile, BESSs along with other electric grid components are leveraging the Internet-of-things paradigm. As a downside, they become vulnerable to cyberattacks. The detection of cyberattacks against BESSs is becoming crucial for system redundancy. We identified a gap in the existing BESS defense research and formulated new types of attacks against a BESS and their detection methods. The attack detection is divided into a forecast-based approach and long-term pattern analysis. We perform a main factor analysis of machine-learning-based methods to forecast the behavior of a BESS. In addition, we observe approaches that can be adapted for the BESS cyber secure design. To provide a thorough investigation, the attacks are classified based on a targeted battery service along with data features that the attack targets.