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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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-networkbased state estimation (SE) forecast are recommended for the implementation in BESSs.
Original languageEnglish
Title of host publicationProceedings of 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering
PublisherIEEE
Publication date2021
Pages188-192
ISBN (Print)978-1-7281-8708-2
DOIs
Publication statusPublished - 2021
Event2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering - On-line, Irvine, United States
Duration: 10 Dec 202012 Dec 2020

Conference

Conference2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering
LocationOn-line
CountryUnited States
CityIrvine
Period10/12/202012/12/2020

Keywords

  • Cyber security
  • Battery energy storage system
  • False data injection attack
  • Cyber threat
  • Cyberattack
  • Battery state estimation
  • Renewable energy source
  • Machine learning
  • Artificial intelligence

Fingerprint Dive into the research topics of 'The Cyber Security of Battery Energy Storage Systems and Adoption of Data-driven Methods'. Together they form a unique fingerprint.

Cite this