Large language model‐enhanced deep reinforcement learning for voltage control in active distribution networks

As active distribution networks (ADNs) become increasingly saturated with intermittent renewable energy sources, maintaining voltage within safe operational limits has become a critical and complex task, requiring advanced control methods beyond traditional rule‐based approaches. This paper proposes a novel voltage control framework that integrates a large language model (LLM) with the soft actor‐critic (SAC) algorithm to enhance the performance of deep reinforcement learning (DRL) in ADNs. The core idea is to leverage the LLM as an external knowledge source during the early stage of training to generate highly interpretable and physically meaningful control action suggestions for key voltage regulation devices. A dynamic action fusion mechanism is designed to combine the LLM‐guided actions with SAC policy outputs. As training progresses, the influence of the LLM gradually diminishes, ultimately yielding a fully autonomous SAC policy. Tests on modified IEEE 33‐ and 123‐bus distribution systems show that the proposed method outperforms the standard SAC in terms of convergence efficiency, voltage deviation reduction and control stability. It effectively mitigates policy oscillations during the early exploration phase and significantly reduces the switching frequency of mechanical devices under the learned policy, thereby contributing to longer equipment service life and higher operational reliability.