Dueling Deep Q-Learning-Based Enhanced Grid Emergency Voltage Stability Control in Power Grids

The recent surge in distributed energy resources has made voltage fluctuations more complex and unpredictable. Consequently, traditional voltage control (VC) methods such as stochastic programming and robust optimization may struggle to manage rapid and significant fluctuations. Facing this challenge, this paper proposes an efficient dueling deep Q network (Dueling DQN)-based autonomous VC method. This study formulates the VC as a Markov decision process and develops an agent that learns optimal operational strategies to maintain voltage levels within safe limits, ensuring grid stability and reliability. The proposed agent operates within the power system environment, designed to mimic real-world grid conditions, including voltage variability and load fluctuations. The Dueling DQN model processes comprehensive observations, including production levels, loads, and voltage measurements, to predict action values that ensure effective VC. The Dueling DQN architecture, training process, and operational mechanisms based on VC are thoroughly detailed. Extensive case studies performed on the modified IEEE 14-bus system and a reduced IEEE 118-bus system and conducted over numerous episodes, demonstrate that the Dueling DQN agent consistently outperforms deep Q networks derivatives and deep deterministic policy gradient approach.