A multi-agent deep reinforcement learning paradigm to improve the robustness and resilience of grid connected electric vehicle charging stations against the destructive effects of cyber-attacks

Reza Sepehrzad, Amin Khodadadi, Sara Adinehpour and Maede Karimi

Energy, 2024, vol. 307, issue C

Abstract: The rising deployment of electric vehicle charging stations (EVCS) in existing power grids and their integration with electric vehicles through communication systems are increasingly vulnerable to cyber-attacks, such as false data injection (FDI) and uncertainties of communication system. This research introduces an EVCS evaluation from a techno-economic standpoint, utilizing the developed and data-oriented TSKFS&MADRL technique to identify and rectify inaccuracies stemming from cyber-attacks like FDI and communication system delays. This method aims to enhance the resilience of EVCSs against sabotage attacks by ensuring fast dynamic responses. Initially, the study models the potential targets of hackers, such as communication systems and transducer sensors, and employs the Euclidean norm theory, weighted least square error method, and residual error technique to identify cyber-attacks resulting from FDI through the comparison of measured data with reference values based on probability distribution functions. Subsequently, the network control and recovery requirements are facilitated by the proposed controller. The proposed approach application has been demonstrated in the IEEE 33 bus, showcasing a 7.33 % lower experimental operation cost compared to the RL method and 12.15 % lower than the CNN method. Additionally, the FDI detection time is observed to be 40 % quicker than alternative methods based on the experimental outcomes.

Keywords: Cyber-attacks; Electric vehicle charging station; False data injection; Communication system latency; Multi-agent deep reinforcement learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224024435
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:307:y:2024:i:c:s0360544224024435

DOI: 10.1016/j.energy.2024.132669

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().