电力工程技术2026，Vol.45Issue(5)：50-60,11.DOI:10.12158/j.2096-3203.2026.05.005
基于混合动作空间深度强化学习的电网潮流收敛性调整
A power flow convergence adjustment based on deep reinforcement learning with hybrid action space
摘要
Abstract
As the operation modes of power systems become increasingly complex,the difficulty of power flow convergence adjustment also increases.Traditional methods that rely on human expertise suffer from delayed response and low efficiency,making them ill-suited for complex scenarios involving diverse and high-dimensional control variables.To address this challenge,a power flow adjustment method based on deep reinforcement learning with a hybrid action space is proposed.Firstly,a power flow convergence discriminator is developed by integrating physical priors with data-driven techniques to enable real-time identification of whether the power flow converges.The output convergence probability is used as a reward guidance signal in deep reinforcement learning.Then,a reinforcement learning environment for convergence adjustment is defined.The state space integrates system-level statistical features with node-level individual features.The action space encompasses both continuous and discrete control variables.And the reward function combines convergence identification results with feedback from the adjustment process to guide policy optimization toward the feasible region.Next,an Actor-Critic network with a hybrid action space is constructed,which hierarchically decouples and models subtasks including device selection,continuous regulation,and discrete control.Finally,simulation analysis and comparative experiments are conducted on the improved IEEE 39-bus and 118-bus systems.The results demonstrate that the power flow convergence discriminator enhanced with physical priors significantly improves both the accuracy and generalization capability of convergence identification compared to traditional models.Furthermore,the proposed coordinated optimization strategy,which integrates continuous-discrete actions,achieves notable improvements in power flow adjustment efficiency and convergence success rate over existing methods.关键词
电网潮流/数据驱动/潮流收敛性鉴别器/混合动作空间/深度强化学习/分层解耦/Actor-Critic架构Key words
power flow/data-driven/power flow convergence discriminator/hybrid action space/deep reinforcement learning/hierarchically decoupled/Actor-Critic architecture分类
信息技术与安全科学引用本文复制引用
吴涛,王昊昊,李天然..基于混合动作空间深度强化学习的电网潮流收敛性调整[J].电力工程技术,2026,45(5):50-60,11.基金项目
智能电网国家科技重大专项资助项目(2024ZD0801103) （2024ZD0801103）
