为提高配电网的运行效率和电压质量，结合深度强化学习和分布式广义快速对偶上升（SAC-GFD）算法，提出了一种基于状态-响应框架的优化策略。首先，利用软演员-评论家（soft actor-critic，SAC）算法将配电网运行优化问题建模为马尔可夫决策过程，智能体在含有可再生能源波动和负荷不确定性的环境中进行交互与探索，获得对不确定性环境具有鲁棒性控制策略。将配电网运行优化问题转化为马尔可夫决策过程，从而训练出能够快速输出配电网设备最优有功功率和无功功率的智能体。其次，计算当前配电网的潮流分布、节点电压状态以及有功功率和无功-电压灵敏度矩阵。然后，用户基于当前配电网状态，采用分布式方法计算自身负荷的最优调整值，确保配电网的安全运行。最后，在IEEE 33节点系统上的仿真结果表明，相较于传统的深度强化学习算法，所提算法能更有效地降低网络损耗和节点电压偏差，且具有更快的训练速度和更好的优化结果。

In order to improve the operational efficiency and voltage quality of distribution networks, an optimization strategy is proposed based on a state-response framework, combining deep reinforcement learning with the distributed generalized fast dual ascent （SAC-GFD） algorithm. Firstly, the soft actor-critic （SAC） algorithm is employed to model the distribution network operation optimization problem as a Markov decision process （MDP）. The agent interacts and explores in an environment with renewable energy fluctuations and load uncertainties, obtaining a control strategy that is robust to uncertain environments. The distribution network operation optimization problem is thus transformed into a Markov decision process, enabling the training of an agent capable of quickly outputting the optimal active and reactive power for distribution network equipment. Secondly, the current distribution network's power flow distribution, node voltage states, and active and reactive voltage sensitivity matrices are calculated. Then, based on the current state of the distribution network, users apply a distributed method to calculate their optimal load adjustments to ensure the safe operation of the network. Finally, the simulation results on the IEEE 33⁃node system demonstrate that compared to traditional deep reinforcement learning algorithms, the proposed method more effectively reduces network losses and node voltage deviations while achieving faster training speeds and better optimization results.