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A decent vehicle-to-grid (V2G) control scheme improves voltage stability and grid reliability of distribution networks (DNs) by providing reactive power and ancillary services. However, unknown malicious attacks in cyber layers and uncertain EV charging/unplugging in physical layers will disturb the measurement process, leading to controller parameters’ mismatching and severe voltage violations. This paper proposes a robust V2G voltage control scheme for DNs investigating cyber attacks and EV customer interruptions. Firstly, the impact of cyber attacks is characterized by time-varying uncertain matrices function with Lebesgur measurable elements, while the EV customer interruptions are represented by Signum variables. Then, a new state-space equation for V2G voltage control in DNs is constructed by incorporating above matrices function and Signum variables. Moreover, a robust voltage controller is proposed for measurement disordered V2G systems to suppress voltage violations while achieving $H_{\infty }$ performance. By utilizing linear matrix inequality (LMI) techniques, sufficient conditions for system closed-loop exponentially mean-square stability and the controller parameter tuning are presented. Case studies conducted on modified IEEE European low voltage test feeder compare two voltage deviation indices of the proposed controller with those of existing methods, verifying the effectiveness of the proposed approach against measurement anomalies.

Cyber attacks targeting the charging process of plug-in electric vehicles (PEVs) have become more frequent with the popularity of PEVs, posing a significant threat to the battery life of PEVs and the stability of the power distribution network system. To mitigate this threat, a cyber attack detection strategy based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Broad Learning System (BLS) is proposed in this paper. The strategy exploits the coupling between PEVs and the power distribution network system during the charging process to achieve the detection of cyber attacks. CEEMDAN is implemented to extract features of system signals, and BLS uses the features extracted by CEEMDAN for feature mapping and enhancement to capture the presence of cyber attacks. Simulation analysis demonstrates that the proposed strategy can achieve more than 85% detection accuracy, with CEEMDAN bringing about 7% improvement in accuracy.