Research on Multi-Agent Event-Triggered Control Algorithms for Power Systems

Multi-agent systems are widely used in modern power systems, but they face challenges such as low data utilization, stringent triggering conditions, and poor environmental adaptability. This study proposes a multi-agent event-triggered control method based on the Proximal Policy Optimization (PPO) policy gradient algorithm. By maximizing the cumulative reward, the agents are driven to learn adaptive triggering strategies, which reduces communication frequency while ensuring system stability. A multi-agent reinforcement learning model is constructed, and the training results show that both the single-episode reward and the average reward significantly increase with the number of training episodes, thus verifying the effectiveness of the algorithm. Based on Lyapunov stability and LaSalle’s invariance principle, an event-triggering threshold is designed using an exponential decay function. Moreover, the sequential decision-making process under uncertain environments is described using the Markov decision process. In the case study with six agents, the triggering conditions effectively constrain the error growth and ensure system stability. The method is further extended to a 33-node power system, where each node is regarded as an agent to simulate voltage fluctuations under load variations. Compared with periodic sampling control, the event-triggered control exhibits faster convergence speed, higher steady-state accuracy, and stronger anti-interference capability, thus confirming its superiority in complex power systems.