Cyber‐attack and defense method for load frequency control in smart grid systems with electric vehicles

Abstract

The latest advancements in Vehicle‐to‐Grid (V2G) technology enable Electric Vehicles (EVs) to contribute to Frequency Regulation (FR), mitigating the impact of uncertain power fluctuations in renewable resources. However, diverse cyber‐attacks pose threats to the Load Frequency Control (LFC) system and smart grid infrastructure, compromising their accuracy and reliability. This research paper focuses on the effects of cyber‐attacks on frequency regulation in a smart grid system that relies on a communication network. The study proposes a two‐area system and a modified IEEE‐39 bus three‐area system, incorporating intermittent solar photovoltaic and wind turbine sources, traditional conventional sources, and electric vehicles. To ensure frequency stabilization and tie‐line power, the researchers introduce a cascade FOPIDN‐(1+TD) controller. Its parameters are optimized using a novel Quasi Opposition Arithmetic Optimization Algorithm (QOAOA). The proposed approach's dynamic response is compared with other commonly used controllers, demonstrating its effectiveness in maintaining stability. The article focuses on the LFC system and presents a novel approach involving a cyber‐physical model. It introduces a method for detecting and defending against cyber‐attacks using deep learning, specifically utilizing the Long‐Short‐Term‐Memory (LSTM) network. The effectiveness of the proposed defense strategy is demonstrated through experiments conducted on both a two‐area interconnected power system and the IEEE‐39 bus system. The outcomes indicate that the suggested defense mechanism effectively maintains acceptable levels of power system frequency and tie‐line power during cyber‐attacks. Additionally, the validity of the controller's performance is verified through real‐time analysis using Hardware‐In‐The‐Loop (HIL) simulation with the OPAL‐RT platform.