The Cyber Security of Battery Energy Storage Systems and Adoption of Data-driven Methods

Abstract

Battery energy storage systems (BESSs) are becoming a crucial part of electric grids due to their important roles in renewable energy sources (RES) integration in energy systems. Cyber-secure operation of BESS in renewable energy systems is significant, since it is susceptible to cyber threats and its potential failure may result in economical and physical damage to both the BESS and the system. However, there is a lack of comprehensive study on the attack detection methods for industrial BESSs. This paper reviews the state-of-the-art work in the area of BESS cyber threats, investigates how to detect cyberattackes in the operation stage. We address the problem of enhancing the communication channels' integrity can by implementing blockchain in the design stage of BESS, combined with applying artificial intelligence (AI) and machine learning (ML) methods for false data injection attack (FDIA) detection in the BESS operation stage. The focus is on the application of ML and AI methods for FDIA detection on different system layers. Based on our analysis, data-driven approaches such as clustering and artificial-neutral-network-based state estimation (SE) forecast are recommended for the implementation in BESSs.