Data-driven load frequency cooperative control for multi-area power system integrated with VSCs and EV aggregators under cyber-attacks

This paper proposes a cooperative load frequency control (LFC) strategy based on a multi-agent deep reinforcement learning (MADRL) framework for the multi-area power system in the presence of voltage source converters (VSCs) and electric vehicle (EV) aggregators under cyber-attacks. Different from the existing LFC model, a novel transfer function of VSCs is first improved by the space-vector technique and integrated with EV aggregators to develop a multi-area training environment. By installing the agent in different control areas and interacting state transition information between agents and the new environment, the MADRL-based control strategy is achieved for centralized training and decentralized execution. Thus, the proposed MADRL method can coordinate thermal turbines, VSCs, as well as EV aggregators in the different control areas. Furthermore, a suitable cyber-attack model that can circumvent bad data detection (BDD) is reconstructed according to the perspective of adversaries for the LFC system. Then the double critic networks and parameter updating policy are designed to eliminate and mitigate the fluctuations caused by cyber-attacks. The comparative simulation with other control strategies on a three-area test power system demonstrates the superior performance of the proposed MADRL-based approach.