Deep Reinforcement Learning-Based Coordinated Voltage Control of HST and DG

With the increasing penetration of distributed generation in active distribution networks, voltage fluctuation and voltage violation problems have become more prominent, especially in weak grids. Conventional voltage control methods based on empirical parameter tuning or single-device regulation often have limited adaptability under time-varying load conditions and fluctuating distributed generation output. To address these issues, this paper proposes a deep reinforcement learning based coordinated voltage control method for HST and DG. The proposed method takes monitored node voltages, load levels, and distributed generation outputs as state inputs and uses the gain adjustments of HST and DG as control actions, thereby achieving adaptive optimization of voltage response through a continuous decision-making framework. On this basis, an experimental validation framework including training and validation datasets, multi-strategy comparison communication-constrained analysis, and repeated-run statistics is established to evaluate the control performance generalization capability and robustness of the proposed method. The results show that the proposed strategy outperforms the baseline method in voltage deviation, overshoot oscillation energy, and control effort, while maintaining good performance consistency in the validation scenario. Under mild communication constraints, the proposed strategy still exhibits acceptable adaptability and operational stability. These findings indicate that deep reinforcement learning provides an effective optimization approach for coordinated HST and DG control and offers a useful reference for voltage regulation in active distribution networks.