A battery energy storage system (BESS) serve an important role in electric grid of a modern time. They allow to mitigate a variability of renewables since their production depends on the wind and sun. The potential failure of a BESS can not only cause an inability to provide required services to a grid but also result in physical and economical damage. BESS contains multiple components that are vulnerable to cyberattacks. Despite, the topic of cybersecurity in the electric grid being widely discussed, utility-scale batteries are currently left apart. During the presentation, I will identify gaps in the current research to understand how BESS work can be corrupted. I have shortlisted the possible cyberattacks that can jeopardize BESS work giving examples of the possible attack against particular BESS services, e.g. frequency regulations. Moreover, I will overview the tools to diminish the possibility of a successful cyberattack on the design and operational stage. I will describe tools that can be used for cyber secure BESS design by protecting communication channels and data storage. Furthermore, we will go through the application of a BESS digital twin for cyberattack detection using Machine learning (ML) and Artificial intelligence (AI) tools. I overview the most applicable ML and AI methods and specific features of their application.

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