AI 中文总结
针对分布式能源资源增加致配电网电压调节难的问题，提出人机协作强化学习框架。该框架结合SAC智能体与自适应拉格朗日约束机制，并融入人类指导模块，在特定环境实现。相比基线方法，显著降低电压违规严重性及功率损耗。
AI 中文摘要
分布式能源资源（DER）渗透率的不断提高增加了配电网的运行变异性，使电压调节变得越来越具有挑战性。传统的深度强化学习（DRL）方法存在不安全的探索行为、收敛速度慢和可靠性有限等问题，限制了其在安全关键型电力系统中的应用。本文提出了一种人机交互式强化学习框架，以提高自主电压调节的安全性和鲁棒性。该方法将软演员评论家（SAC）智能体与自适应拉格朗日约束机制相结合，以确保电压限制，同时人类指导模块通过协调电容器组和电池储能系统（BESS）调度提供基于灵敏度的校正。这些校正被纳入人类正则化的智能体损失中，使策略能够内化安全且可解释的控制行为。该框架在PowerGym-OpenDSS环境中使用IEEE 13节点馈线实现。仿真结果表明，与其他基线方法相比，所提出的人机交互式拉格朗日SAC（HI-LSAC）实现了显著更低的电压违规严重性和更低的功率损耗。
英文摘要
The growing penetration of distributed energy resources (DERs) has increased the operational variability of distribution networks, making voltage regulation increasingly challenging. Conventional deep reinforcement learning (DRL) methods exhibit unsafe exploration behavior, slow convergence, and limited reliability, which restrict their applicability in safety-critical power system settings. This paper presents a human-interactive reinforcement learning framework that enhances the safety and robustness of autonomous voltage regulation. The proposed approach integrates a Soft Actor-Critic (SAC) agent with an adaptive Lagrange constraint mechanism to enforce voltage limits, while a human-guidance module provides sensitivity-based corrections through coordinated capacitor-bank and Battery Energy Storage System (BESS) dispatch. These corrections are incorporated into a human-regularized actor loss, enabling the policy to internalize safe and interpretable control behavior. The framework is implemented in the PowerGym-OpenDSS environment using the IEEE 13-node feeder. Simulation results show that the proposed Human-Interactive Lagrangian SAC (HI-LSAC) achieves significantly lower voltage-violation severity and reduced power losses compared with the other baseline methods.
Comments10 pages, 3 figures
