Enhancing Cyber-Resilience in Electric Vehicle Charging Stations: A Multi-Agent Deep Reinforcement Learning Approach

Electric vehicle charging stations (EVCSs) heavily rely on communication systems, making them vulnerable to cyber uncertainties such as communication delays and False Data Injection (FDI) attacks. In this study, the techno-economic evaluation of the EVCS based on the developed and data-driven Takagi-Sugeno-Kang Fuzzy System & Multi-Agent Deep Reinforcement Learning (TSKFS&MADRL) method is presented to detect and compensate for cyber uncertainties such as FDI attacks and communication delay. In addition, the proposed approach provides a fast dynamic response and enhances the resilient operation of EVCS in the presence of FDI attacks. First, the target points of hackers such as communication systems and transducer sensors are modeled, and then, using the Euclidean norm theory, weighted least square error method, and residual error technique resulting from comparing measured data with reference values based on probability distribution functions, FDI cyber-attacks are detected. Then, the network control and recovery requirements are enabled by the proposed controller based on the TSKFS&MADRL method. The proposed approach has been implemented in the IEEE 33 bus network. The experimental operating cost is 7.33% less than the RL method and 12.15% less than the CNN method. Also, the experimental results of the proposed method show a 40% detection time reduction compared to other methods.